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Feature Fusion for Online Mutual Knowledge Distillation

Jangho Kim, Minsung Hyun, Inseop Chung, Nojun Kwak

Auto-TLDR; Feature Fusion Learning Using Fusion of Sub-Networks

Abstract Slides Poster

We propose a learning framework named Feature Fusion Learning (FFL) that efficiently trains a powerful classifier through a fusion module which combines the feature maps generated from parallel neural networks and generates meaningful feature maps. Specifically, we train a number of parallel neural networks as sub-networks, then we combine the feature maps from each sub-network using a fusion module to create a more meaningful feature map. The fused feature map is passed into the fused classifier for overall classification. Unlike existing feature fusion methods, in our framework, an ensemble of sub-network classifiers transfers its knowledge to the fused classifier and then the fused classifier delivers its knowledge back to each sub-network, mutually teaching one another in an online-knowledge distillation manner. This mutually teaching system not only improves the performance of the fused classifier but also obtains performance gain in each sub-network. Moreover, our model is more beneficial than other alternative methods because different types of network can be used for each sub-network. We have performed a variety of experiments on multiple datasets such as CIFAR-10, CIFAR-100 and ImageNet and proved that our method is more effective than other alternative methods in terms of performances of both sub-networks and the fused classifier, and the aspect of generating meaningful feature maps.

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Knowledge Distillation Beyond Model Compression

Fahad Sarfraz, Elahe Arani, Bahram Zonooz

Auto-TLDR; Knowledge Distillation from Teacher to Student

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Knowledge distillation (KD) is commonly deemed as an effective model compression technique in which a compact model (student) is trained under the supervision of a larger pretrained model or an ensemble of models (teacher). Various techniques have been proposed since the original formulation, which mimics different aspects of the teacher such as the representation space, decision boundary or intra-data relationship. Some methods replace the one way knowledge distillation from a static teacher with collaborative learning between a cohort of students. Despite the recent advances, a clear understanding of where knowledge resides in a deep neural network and optimal method for capturing knowledge from teacher and transferring it to student still remains an open question. In this study we provide an extensive study on 9 different knowledge distillation methods which covers a broad spectrum of approaches to capture and transfer knowledge. We demonstrate the versatility of the KD framework on different datasets and network architectures under varying capacity gaps between the teacher and student. The study provides intuition for the effects of mimicking different aspects of the teacher and derives insights from the performance of the different distillation approaches to guide the the design of more effective KD methods . Furthermore, our study shows the effectiveness of the KD framework in learning efficiently under varying severity levels of label noise and class imbalance, consistently providing significant generalization gains over standard training. We emphasize that the efficacy of KD goes much beyond a model compression technique and should be considered as a general purpose training paradigm which offers more robustness to common challenges in the real-world datasets compared to the standard training procedure.

Efficient Online Subclass Knowledge Distillation for Image Classification

Maria Tzelepi, Nikolaos Passalis, Anastasios Tefas

Auto-TLDR; OSKD: Online Subclass Knowledge Distillation

Abstract Slides Poster Similar

Deploying state-of-the-art deep learning models on embedded systems dictates certain storage and computation limitations. During the recent few years Knowledge Distillation (KD) has been recognized as a prominent approach to address this issue. That is, KD has been effectively proposed for training fast and compact deep learning models by transferring knowledge from more complex and powerful models. However, knowledge distillation, in its conventional form, involves multiple stages of training, rendering it a computationally and memory demanding procedure. In this paper, a novel single-stage self knowledge distillation method is proposed, namely Online Subclass Knowledge Distillation (OSKD), that aims at revealing the similarities inside classes, improving the performance of any deep neural model in an online manner. Hence, as opposed to existing online distillation methods, we are able to acquire further knowledge from the model itself, without building multiple identical models or using multiple models to teach each other, rendering the OSKD approach more efficient. The experimental evaluation on two datasets validates that the proposed method improves the classification performance.

Multi-Order Feature Statistical Model for Fine-Grained Visual Categorization

Qingtao Wang, Ke Zhang, Shaoli Huang, Lianbo Zhang, Jin Fan

Auto-TLDR; Multi-Order Feature Statistical Method for Fine-Grained Visual Categorization

Abstract Slides Poster Similar

Fine-grained visual categorization aims to learn a robust image representation modeling subtle differences from similar categories. Existing methods in this field tackle the problem by designing complex frameworks, which produce high-level features by performing first-order or second-order pooling. Despite the impressive performance achieved by these strategies, the single-order networks only carry linear or non-linear information of the last convolutional layer, neglecting the fact that feature from different orders are mutually complementary. In this paper, we propose a Multi-Order Feature Statistical Method (MOFS), which learns fine-grained features characterizing multiple orders. Specifically, the MOFS consists of two sub-modules: (i) a first-order module modeling both mid-level and high-level features. (ii) a covariance feature statistical module capturing high-order features. By deploying these two sub-modules on the top of existing backbone networks, MOFS simultaneously captures multi-level of discrimative patters including local, global and co-related patters. We evaluate the proposed method on three challenging benchmarks, namely CUB-200-2011, Stanford Cars, and FGVC-Aircraft. Compared with state-of-the-art methods, experiments results exhibit superior performance in recognizing fine-grained objects

Channel Planting for Deep Neural Networks Using Knowledge Distillation

Kakeru Mitsuno, Yuichiro Nomura, Takio Kurita

Auto-TLDR; Incremental Training for Deep Neural Networks with Knowledge Distillation

Abstract Slides Poster Similar

In recent years, deeper and wider neural networks have shown excellent performance in computer vision tasks, while their enormous amount of parameters results in increased computational cost and overfitting. Several methods have been proposed to compress the size of the networks without reducing network performance. Network pruning can reduce redundant and unnecessary parameters from a network. Knowledge distillation can transfer the knowledge of deeper and wider networks to smaller networks. The performance of the smaller network obtained by these methods is bounded by the predefined network. Neural architecture search has been proposed, which can search automatically the architecture of the networks to break the structure limitation. Also, there is a dynamic configuration method to train networks incrementally as sub-networks. In this paper, we present a novel incremental training algorithm for deep neural networks called planting. Our planting can search the optimal network architecture with smaller number of parameters for improving the network performance by augmenting channels incrementally to layers of the initial networks while keeping the earlier trained parameters fixed. Also, we propose using the knowledge distillation method for training the channels planted. By transferring the knowledge of deeper and wider networks, we can grow the networks effectively and efficiently. We evaluate the effectiveness of the proposed method on different datasets such as CIFAR-10/100 and STL-10. For the STL-10 dataset, we show that we are able to achieve comparable performance with only 7% parameters compared to the larger network and reduce the overfitting caused by a small amount of the data.

Knowledge Distillation with a Precise Teacher and Prediction with Abstention

Xu Yi, Jian Pu, Hui Zhao

Auto-TLDR; Knowledge Distillation using Deep gambler loss and selective classification framework

Abstract Slides Poster Similar

Knowledge distillation, which aims to train model under the supervision from another large model (teacher model) to the original model (student model), has achieved remarkable results in supervised learning. However, there are two major problems with existing knowledge distillation methods. One is the teacher's supervision is sometimes misleading, and the other is the student's prediction is not accurate enough. To address the first issue, instead of learning a combination of both teachers and ground truth, we apply knowledge adjustment to correct teachers' supervision using ground truth. For the second problem, we use the selective classification framework to train the student model. In particular, the deep gambler loss is adopted to predict with reservation by explicitly introducing the $(m+1)$-th class. We consider two settings of knowledge distillation: (1) distillation across different network structures ({\it AlexNet, ResNet}), and (2) distillation across networks with different depths ({\it ResNet18, ResNet50}) to evaluate the effectiveness of our method. The experimental results on benchmark datasets (i.e., {\it Fashion-MNIST, SVHN, CIFAR10, CIFAR100}) are reported with higher prediction accuracies and lower coverage errors.

Compact CNN Structure Learning by Knowledge Distillation

Waqar Ahmed, Andrea Zunino, Pietro Morerio, Vittorio Murino

Auto-TLDR; Knowledge Distillation for Compressing Deep Convolutional Neural Networks

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The concept of compressing deep Convolutional Neural Networks (CNNs) is essential to use limited computation, power, and memory resources on embedded devices. However, existing methods achieve this objective at the cost of a drop in inference accuracy in computer vision tasks. To address such a drawback, we propose a framework that leverages knowledge distillation along with customizable block-wise optimization to learn a lightweight CNN structure while preserving better control over the compression-performance tradeoff. Considering specific resource constraints, e.g., floating-point operations per second (FLOPs) or model-parameters, our method results in a state of the art network compression while being capable of achieving better inference accuracy. In a comprehensive evaluation, we demonstrate that our method is effective, robust, and consistent with results over a variety of network architectures and datasets, at negligible training overhead. In particular, for the already compact network MobileNet_v2, our method offers up to 2x and 5.2x better model compression in terms of FLOPs and model-parameters, respectively, while getting 1.05% better model performance than the baseline network.

FastSal: A Computationally Efficient Network for Visual Saliency Prediction

Feiyan Hu, Kevin Mcguinness

Auto-TLDR; MobileNetV2: A Convolutional Neural Network for Saliency Prediction

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This paper focuses on the problem of visual saliency prediction, predicting regions of an image that tend to attract human visual attention, under a constrained computational budget. We modify and test various recent efficient convolutional neural network architectures like EfficientNet and MobileNetV2 and compare them with existing state-of-the-art saliency models such as SalGAN and DeepGaze II both in terms of standard accuracy metrics like AUC and NSS, and in terms of the computational complexity and model size. We find that MobileNetV2 makes an excellent backbone for a visual saliency model and can be effective even without a complex decoder. We also show that knowledge transfer from a more computationally expensive model like DeepGaze II can be achieved via pseudo-labelling an unlabelled dataset, and that this approach gives result on-par with many state-of-the-art algorithms with a fraction of the computational cost and model size.

Slimming ResNet by Slimming Shortcut

Donggyu Joo, Doyeon Kim, Junmo Kim

Auto-TLDR; SSPruning: Slimming Shortcut Pruning on ResNet Based Networks

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Conventional network pruning methods on convolutional neural networks (CNNs) reduce the number of input or output channels of convolution layers. With these approaches, the channels in the plain network can be pruned without any restrictions. However, in case of the ResNet based networks which have shortcuts (skip connections), the channel slimming of existing pruning methods is limited to the inside of each residual block. Since the number of Flops and parameters are also highly related to the number of channels in the shortcuts, more investigation on pruning channels in shortcuts is required. In this paper, we propose a novel pruning method, Slimming Shortcut Pruning (SSPruning), for pruning channels in shortcuts on ResNet based networks. First, we separate the long shortcut in individual regions that can be pruned independently without considering its long connections. Then, by applying our Importance Learning Gate (ILG) which learns the importance of channels globally regardless of channel type and location (i.e., in the shortcut or inside of the block), we can finally achieve an optimally pruned model. Through various experiments, we have confirmed that our method yields outstanding results when we prune the shortcuts and inside of the block together.

Dynamic Multi-Path Neural Network

Yingcheng Su, Yichao Wu, Ken Chen, Ding Liang, Xiaolin Hu

Auto-TLDR; Dynamic Multi-path Neural Network

Abstract Slides Similar

Although deeper and larger neural networks have achieved better performance, due to overwhelming burden on computation, they cannot meet the demands of deployment on resource-limited devices. An effective strategy to address this problem is to make use of dynamic inference mechanism, which changes the inference path for different samples at runtime. Existing methods only reduce the depth by skipping an entire specific layer, which may lose important information in this layer. In this paper, we propose a novel method called Dynamic Multi-path Neural Network (DMNN), which provides more topology choices in terms of both width and depth on the fly. For better modelling the inference path selection, we further introduce previous state and object category information to guide the training process. Compared to previous dynamic inference techniques, the proposed method is more flexible and easier to incorporate into most modern network architectures. Experimental results on ImageNet and CIFAR-100 demonstrate the superiority of our method on both efficiency and classification accuracy.

Distilling Spikes: Knowledge Distillation in Spiking Neural Networks

Ravi Kumar Kushawaha, Saurabh Kumar, Biplab Banerjee, Rajbabu Velmurugan

Auto-TLDR; Knowledge Distillation in Spiking Neural Networks for Image Classification

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Spiking Neural Networks (SNN) are energy-efficient computing architectures that exchange spikes for processing information, unlike classical Artificial Neural Networks (ANN). Due to this, SNNs are better suited for real-life deployments. However, similar to ANNs, SNNs also benefit from deeper architectures to obtain improved performance. Furthermore, like the deep ANNs, the memory, compute and power requirements of SNNs also increase with model size, and model compression becomes a necessity. Knowledge distillation is a model com- pression technique that enables transferring the learning of a large machine learning model to a smaller model with minimal loss in performance. In this paper, we propose techniques for knowledge distillation in spiking neural networks for the task of image classification. We present ways to distill spikes from a larger SNN, also called the teacher network, to a smaller one, also called the student network, while minimally impacting the classification accuracy. We demonstrate the effectiveness of the proposed method with detailed experiments on three standard datasets while proposing novel distillation methodologies and loss functions. We also present a multi-stage knowledge distillation technique for SNNs using an intermediate network to obtain higher performance from the student network. Our approach is expected to open up new avenues for deploying high performing large SNN models on resource-constrained hardware platforms.

Towards Low-Bit Quantization of Deep Neural Networks with Limited Data

Yong Yuan, Chen Chen, Xiyuan Hu, Silong Peng

Auto-TLDR; Low-Precision Quantization of Deep Neural Networks with Limited Data

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Recent machine learning methods use increasingly large deep neural networks to achieve state-of-the-art results in various tasks. Network quantization can effectively reduce computation and memory costs without modifying network structures, facilitating the deployment of deep neural networks (DNNs) on cloud and edge devices. However, most of the existing methods usually need time-consuming training or fine-tuning and access to the original training dataset that may be unavailable due to privacy or security concerns. In this paper, we present a novel method to achieve low-precision quantization of deep neural networks with limited data. Firstly, to reduce the complexity of per-channel quantization and the degeneration of per-layer quantization, we introduce group-wise quantization which separates the output channels into groups that each group is quantized separately. Secondly, to better distill knowledge from the pre-trained FP32 model with limited data, we introduce a two-stage knowledge distillation method that divides the optimization process into independent optimization stage and joint optimization stage to address the limitation of layer-wise supervision and global supervision. Extensive experiments on ImageNet2012 (ResNet18/50, ShuffleNetV2, and MobileNetV2) demonstrate that the proposed approach can significantly improve the quantization model's accuracy when only a few training samples are available. We further show that the method also extends to other computer vision architectures and tasks such as object detection and semantic segmentation.

Feature-Dependent Cross-Connections in Multi-Path Neural Networks

Dumindu Tissera, Kasun Vithanage, Rukshan Wijesinghe, Kumara Kahatapitiya, Subha Fernando, Ranga Rodrigo

Auto-TLDR; Multi-path Networks for Adaptive Feature Extraction

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Learning a particular task from a dataset, samples in which originate from diverse contexts, is challenging, and usually addressed by deepening or widening standard neural networks. As opposed to conventional network widening, multi-path architectures restrict the quadratic increment of complexity to a linear scale. However, existing multi-column/path networks or model ensembling methods do not consider any feature-dependant allocation of parallel resources, and therefore, tend to learn redundant features. Given a layer in a multi-path network, if we restrict each path to learn a context-specific set of features and introduce a mechanism to intelligently allocate incoming feature maps to such paths, each path can specialize in a certain context, reducing the redundancy and improving the quality of extracted features. This eventually leads to better-optimized usage of parallel resources. To do this, we propose inserting feature-dependant cross-connections between parallel sets of feature maps in successive layers. The weights of these cross-connections are learned based on the input features of the particular layer. Our multi-path networks show improved image recognition accuracy at a similar complexity compared to conventional and state-of-the-art methods for deepening, widening and adaptive feature extracting, in both small and large scale datasets.

Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural Networks

Sebastian Palacio, Philipp Engler, Jörn Hees, Andreas Dengel

Auto-TLDR; Self-Supervised Autogenous Learning for Deep Neural Networks

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Classification problems solved with deep neural networks (DNNs) typically rely on a closed world paradigm, and optimize over a single objective (e.g., minimization of the cross- entropy loss). This setup dismisses all kinds of supporting signals that can be used to reinforce the existence or absence of particular patterns. The increasing need for models that are interpretable by design makes the inclusion of said contextual signals a crucial necessity. To this end, we introduce the notion of Self-Supervised Autogenous Learning (SSAL). A SSAL objective is realized through one or more additional targets that are derived from the original supervised classification task, following architectural principles found in multi-task learning. SSAL branches impose low-level priors into the optimization process (e.g., grouping). The ability of using SSAL branches during inference, allow models to converge faster, focusing on a richer set of class-relevant features. We equip state-of-the-art DNNs with SSAL objectives and report consistent improvements for all of them on CIFAR100 and Imagenet. We show that SSAL models outperform similar state-of-the-art methods focused on contextual loss functions, auxiliary branches and hierarchical priors.

Context-Aware Residual Module for Image Classification

Jing Bai, Ran Chen

Auto-TLDR; Context-Aware Residual Module for Image Classification

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Attention module has achieved great success in numerous vision tasks. However, existing visual attention modules generally consider the features of a single-scale, and cannot make full use of their multi-scale contextual information. Meanwhile, the multi-scale spatial feature representation has demonstrated its outstanding performance in a wide range of applications. However, the multi-scale features are always represented in a layer-wise manner, i.e. it is impossible to know their contextual information at a granular level. Focusing on the above issue, a context-aware residual module for image classification is proposed in this paper. It consists of a novel multi-scale channel attention module MSCAM to learn refined channel weights by considering the visual features of its own scale and its surrounding fields, and a multi-scale spatial aware module MSSAM to further capture more spatial information. Either or both of the two modules can be plugged into any CNN-based backbone image classification architecture with a short residual connection to obtain the context-aware enhanced features. The experiments on public image recognition datasets including CIFAR10, CIFAR100，Tiny-ImageNet and ImageNet consistently demonstrate that our proposed modules significantly outperforms a wide-used state-of-the-art methods, e.g., ResNet and the lightweight networks of MobileNet and SqueezeeNet.

Teacher-Student Training and Triplet Loss for Facial Expression Recognition under Occlusion

Mariana-Iuliana Georgescu, Radu Ionescu

Auto-TLDR; Knowledge Distillation for Facial Expression Recognition under Occlusion

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In this paper, we study the task of facial expression recognition under strong occlusion. We are particularly interested in cases where 50% of the face is occluded, e.g. when the subject wears a Virtual Reality (VR) headset. While previous studies show that pre-training convolutional neural networks (CNNs) on fully-visible (non-occluded) faces improves the accuracy, we propose to employ knowledge distillation to achieve further improvements. First of all, we employ the classic teacher-student training strategy, in which the teacher is a CNN trained on fully-visible faces and the student is a CNN trained on occluded faces. Second of all, we propose a new approach for knowledge distillation based on triplet loss. During training, the goal is to reduce the distance between an anchor embedding, produced by a student CNN that takes occluded faces as input, and a positive embedding (from the same class as the anchor), produced by a teacher CNN trained on fully-visible faces, so that it becomes smaller than the distance between the anchor and a negative embedding (from a different class than the anchor), produced by the student CNN. Third of all, we propose to combine the distilled embeddings obtained through the classic teacher-student strategy and our novel teacher-student strategy based on triplet loss into a single embedding vector. We conduct experiments on two benchmarks, FER+ and AffectNet, with two CNN architectures, VGG-f and VGG-face, showing that knowledge distillation can bring significant improvements over the state-of-the-art methods designed for occluded faces in the VR setting. Furthermore, we obtain accuracy rates that are quite close to the state-of-the-art models that take as input fully-visible faces. For example, on the FER+ data set, our VGG-face based on concatenated distilled embeddings attains an accuracy rate of 82.75% on lower-half-visible faces, which is only 2.24% below the accuracy rate of a state-of-the-art VGG-13 that is evaluated on fully-visible faces. Given that our model sees only the lower-half of the face, we consider this to be a remarkable achievement. In conclusion, we consider that our distilled CNN models can provide useful feedback for the task of recognizing the facial expressions of a person wearing a VR headset.

Efficient-Receptive Field Block with Group Spatial Attention Mechanism for Object Detection

Jiacheng Zhang, Zhicheng Zhao, Fei Su

Auto-TLDR; E-RFB: Efficient-Receptive Field Block for Deep Neural Network for Object Detection

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Object detection has been paid rising attention in computer vision field. Convolutional Neural Networks (CNNs) extract high-level semantic features of images, which directly determine the performance of object detection. As a common solution, embedding integration modules into CNNs can enrich extracted features and thereby improve the performance. However, the instability and inconsistency of internal multiple branches exist in these modules. To address this problem, we propose a novel multibranch module called Efficient-Receptive Field Block (E-RFB), in which multiple levels of features are combined for network optimization. Specifically, by downsampling and increasing depth, the E-RFB provides sufficient RF. Second, in order to eliminate the inconsistency across different branches, a novel spatial attention mechanism, namely, Group Spatial Attention Module (GSAM) is proposed. The GSAM gradually narrows a feature map by channel grouping; thus it encodes the information between spatial and channel dimensions into the final attention heat map. Third, the proposed module can be easily joined in various CNNs to enhance feature representation as a plug-and-play component. With SSD-style detectors, our method halves the parameters of the original detection head and achieves high accuracy on the PASCAL VOC and MS COCO datasets. Moreover, the proposed method achieves superior performance compared with state-of-the-art methods based on similar framework.

A Boundary-Aware Distillation Network for Compressed Video Semantic Segmentation

Hongchao Lu

Auto-TLDR; A Boundary-Aware Distillation Network for Video Semantic Segmentation

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In recent years optical flow is often estimated to reuse features so as to accelerate video semantic segmentation. With addition of optical flow network, however, extra cost may incur and accuracy may thus be degraded because of repeated warping operation. In this paper, we propose a boundary-aware distillation network (BDNet) that replaces optical flow network with block motion vectors encoded in compressed video, resulting in negligible computational complexity. In order to make salient features, an auxiliary boundary-aware stream is added to the main stream to jointly estimate silhouette and segmentation of objects. To further correct warped features, a well-trained teacher network is employed to transfer knowledge to the main stream. Both boundary-aware stream and the teacher network are neglected during inference stage, so that video segmentation network enables to get faster without increasing any computational burden. By splitting the task into three components, our BDNet shows almost 10% time saving as well as 1.6% accuracy improvement over baseline on the Cityscapes dataset.

Adversarial Knowledge Distillation for a Compact Generator

Hideki Tsunashima, Shigeo Morishima, Junji Yamato, Qiu Chen, Hirokatsu Kataoka

Auto-TLDR; Adversarial Knowledge Distillation for Generative Adversarial Nets

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In this paper, we propose memory-efficient Generative Adversarial Nets (GANs) in line with knowledge distillation. Most existing GANs have a shortcoming in terms of the number of model parameters and low processing speed. Here, to tackle the problem, we propose Adversarial Knowledge Distillation for Generative models (AKDG) for highly efficient GANs, in terms of unconditional generation. Using AKDG, model size and processing speed are substantively reduced. Through an adversarial training exercise with a distillation discriminator, a student generator successfully mimics a teacher generator in fewer model layers and fewer parameters and at a higher processing speed. Moreover, our AKDG is network architecture-agnostic. Comparison of AKDG-applied models to vanilla models suggests that it achieves closer scores to a teacher generator and more efficient performance than a baseline method with respect to Inception Score (IS) and Frechet Inception Distance (FID). In CIFAR-10 experiments, improving IS/FID 1.17pt/55.19pt and in LSUN bedroom experiments, improving FID 71.1pt in comparison to the conventional distillation method for GANs.

Adaptive Image Compression Using GAN Based Semantic-Perceptual Residual Compensation

Ruojing Wang, Zitang Sun, Sei-Ichiro Kamata, Weili Chen

Auto-TLDR; Adaptive Image Compression using GAN based Semantic-Perceptual Residual Compensation

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Image compression is a basic task in image processing. In this paper, We present an adaptive image compression algorithm that relies on GAN based semantic-perceptual residual compensation, which is available to offer visually pleasing reconstruction at a low bitrate. Our method adopt an U-shaped encoding and decoding structure accompanied by a well-designed dense residual connection with strip pooling module to improve the original auto-encoder. Besides, we introduce the idea of adversarial learning by introducing a discriminator thus constructed a complete GAN. To improve the coding efficiency, we creatively designed an adaptive semantic-perception residual compensation block based on Grad-CAM algorithm. In the improvement of the quantizer, we embed the method of soft-quantization so as to solve the problem to some extent that back propagation process is irreversible. Simultaneously, we use the latest FLIF lossless compression algorithm and BPG vector compression algorithm to perform deeper compression on the image. More importantly experimental results including PSNR, MS-SSIM demonstrate that the proposed approach outperforms the current state-of-the-art image compression methods.

Real-Time Semantic Segmentation Via Region and Pixel Context Network

Yajun Li, Yazhou Liu, Quansen Sun

Auto-TLDR; A Dual Context Network for Real-Time Semantic Segmentation

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Real-time semantic segmentation is a challenging task as both segmentation accuracy and inference speed need to be considered at the same time. In this paper, we present a Dual Context Network (DCNet) to address this challenge. It contains two independent sub-networks: Region Context Network and Pixel Context Network. Region Context Network is main network with low-resolution input and feature re-weighting module to achieve sufficient receptive field. Meanwhile, Pixel Context Network with location attention module to capture the location dependencies of each pixel for assisting the main network to recover spatial detail. A contextual feature fusion is introduced to combine output features of these two sub-networks. The experiments show that DCNet can achieve high-quality segmentation while keeping a high speed. Specifically, for Cityscapes test dataset, we achieve 76.1% Mean IOU with the speed of 82 FPS on a single GTX 2080Ti GPU when using ResNet50 as backbone, and 71.2% Mean IOU with the speed of 142 FPS when using ResNet18 as backbone.

Attention Pyramid Module for Scene Recognition

Zhinan Qiao, Xiaohui Yuan, Chengyuan Zhuang, Abolfazl Meyarian

Auto-TLDR; Attention Pyramid Module for Multi-Scale Scene Recognition

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The unrestricted open vocabulary and diverse substances of scenery images bring significant challenges to scene recognition. However, most deep learning architectures and attention methods are developed on general-purpose datasets and omit the characteristics of scene data. In this paper, we exploit the attention pyramid module (APM) to tackle the predicament of scene recognition. Our method streamlines the multi-scale scene recognition pipeline, learns comprehensive scene features at various scales and locations, addresses the interdependency among scales, and further assists feature re-calibration as well as aggregation process. APM is extremely light-weighted and can be easily plugged into existing network architectures in a parameter-efficient manner. By simply integrating APM into ResNet-50, we obtain a 3.54\% boost in terms of top-1 accuracy on the benchmark scene dataset. Comprehensive experiments show that APM achieves better performance comparing with state-of-the-art attention methods using significant less computation budget. Code and pre-trained models will be made publicly available.

Stochastic Label Refinery: Toward Better Target Label Distribution

Xi Fang, Jiancheng Yang, Bingbing Ni

Auto-TLDR; Stochastic Label Refinery for Deep Supervised Learning

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This paper proposes a simple yet effective strategy for improving deep supervised learning, named Stochastic Label Refinery (SLR), by refining training labels to more informative labels. When training a neural network, target distributions (or ground-truth) are typically "hard", which means the target label of each category consists of only 0 and 1. However, the fixed "hard" target distributions do not capture association between categories or that between objects. In this study, instead of using the hard target distributions, we iteratively generate "soft" target label distributions for training the neural networks, which leads to better performances. The soft target distributions are obtained via an Expectation-Maximization (EM) iteration, where the "true" target distributions and the learned models are regarded as hidden variables. In E step, the models are optimized to approximate the target distributions on stochastic splits of training data; In M step, the target distributions are updated with predicted pseudo-label on leave-out splits. Extensive experiments on classification and ordinal regression tasks, empirically prove that the refined target distribution consistently leads to considerable performance improvements even applied on competitive baselines. Notably, in DeepDR 2020 Diabetic Retinopathy Grading (DeepDRiD) challenge, our method improves the quadratic weighted kappa on official validation set from 0.8247 to 0.8348 and achieves a state-of-the-art score on online test set. The proposed SLR technique is easy to implement and practically applicable. The code will be open sourced soon.

Fast and Accurate Real-Time Semantic Segmentation with Dilated Asymmetric Convolutions

Leonel Rosas-Arias, Gibran Benitez-Garcia, Jose Portillo-Portillo, Gabriel Sanchez-Perez, Keiji Yanai

Auto-TLDR; FASSD-Net: Dilated Asymmetric Pyramidal Fusion for Real-Time Semantic Segmentation

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Recent works have shown promising results applied to real-time semantic segmentation tasks. To maintain fast inference speed, most of the existing networks make use of light decoders, or they simply do not use them at all. This strategy helps to maintain a fast inference speed; however, their accuracy performance is significantly lower in comparison to non-real-time semantic segmentation networks. In this paper, we introduce two key modules aimed to design a high-performance decoder for real-time semantic segmentation for reducing the accuracy gap between real-time and non-real-time segmentation networks. Our first module, Dilated Asymmetric Pyramidal Fusion (DAPF), is designed to substantially increase the receptive field on the top of the last stage of the encoder, obtaining richer contextual features. Our second module, Multi-resolution Dilated Asymmetric (MDA) module, fuses and refines detail and contextual information from multi-scale feature maps coming from early and deeper stages of the network. Both modules exploit contextual information without excessively increasing the computational complexity by using asymmetric convolutions. Our proposed network entitled “FASSD-Net” reaches 78.8% of mIoU accuracy on the Cityscapes validation dataset at 41.1 FPS on full resolution images (1024x2048). Besides, with a light version of our network, we reach 74.1% of mIoU at 133.1 FPS (full resolution) on a single NVIDIA GTX 1080Ti card with no additional acceleration techniques. The source code and pre-trained models are available at https://github.com/GibranBenitez/FASSD-Net.

Automatic Student Network Search for Knowledge Distillation

Zhexi Zhang, Wei Zhu, Junchi Yan, Peng Gao, Guotong Xie

Auto-TLDR; NAS-KD: Knowledge Distillation for BERT

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Pre-trained language models (PLMs), such as BERT, have achieved outstanding performance on multiple natural language processing (NLP) tasks. However, such pre-trained models usually contain a huge number of parameters and are computationally expensive. The high resource demand hinders their application on resource-restricted devices like mobile phones. Knowledge distillation (KD) is an effective compression approach, aiming at encouraging a light-weight student network to imitate the teacher network, and accordingly latent knowledge is transferred from the teacher to student. However, the great majority of student networks in previous KD methods are manually designed, normally a subnetwork of the teacher network. Transformer is generally utilized as the student for compressing BERT but still contains masses of parameters. Motivated by this, we propose a novel approach named NAS-KD, which automatically generates an optimal student network using neural architecture search (NAS) to enhance the distillation for BERT. Experiment on 7 classification tasks in NLP domain demonstrates that NAS-KD can substantially reduce the size of BERT without much performance sacrifice.

On the Information of Feature Maps and Pruning of Deep Neural Networks

Mohammadreza Soltani, Suya Wu, Jie Ding, Robert Ravier, Vahid Tarokh

Auto-TLDR; Compressing Deep Neural Models Using Mutual Information

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A technique for compressing deep neural models achieving competitive performance to state-of-the-art methods is proposed. The approach utilizes the mutual information between the feature maps and the output of the model in order to prune the redundant layers of the network. Extensive numerical experiments on both CIFAR-10, CIFAR-100, and Tiny ImageNet data sets demonstrate that the proposed method can be effective in compressing deep models, both in terms of the numbers of parameters and operations. For instance, by applying the proposed approach to DenseNet model with 0.77 million parameters and 293 million operations for classification of CIFAR-10 data set, a reduction of 62.66% and 41.00% in the number of parameters and the number of operations are respectively achieved, while increasing the test error only by less than 1%.

Learning a Dynamic High-Resolution Network for Multi-Scale Pedestrian Detection

Mengyuan Ding, Shanshan Zhang, Jian Yang

Auto-TLDR; Learningable Dynamic HRNet for Pedestrian Detection

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Pedestrian detection is a canonical instance of object detection in computer vision. In practice, scale variation is one of the key challenges, resulting in unbalanced performance across different scales. Recently, the High-Resolution Network (HRNet) has become popular because high-resolution feature representations are more friendly to small objects. However, when we apply HRNet for pedestrian detection, we observe that it improves for small pedestrians on one hand, but hurts the performance for larger ones on the other hand. To overcome this problem, we propose a learnable Dynamic HRNet (DHRNet) aiming to generate different network paths adaptive to different scales. Specifically, we construct a parallel multi-branch architecture and add a soft conditional gate module allowing for dynamic feature fusion. Both branches share all the same parameters except the soft gate module. Experimental results on CityPersons and Caltech benchmarks indicate that our proposed dynamic HRNet is more capable of dealing with pedestrians of various scales, and thus improves the performance across different scales consistently.

SFPN: Semantic Feature Pyramid Network for Object Detection

Yi Gan, Wei Xu, Jianbo Su

Auto-TLDR; SFPN: Semantic Feature Pyramid Network to Address Information Dilution Issue in FPN

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Feature Pyramid Network(FPN) employs a top-down path to enhance low level feature by utilizing high level feature.However, further improvement of detector is greatly hindered by the inner defect of FPN. The dilution issue in FPN is analyzed in this paper, and a new architecture named Semantic Feature Pyramid Network(SFPN) is introduced to address the information imbalance problem caused by information dilution. The proposed method consists of two simple and effective components: Semantic Pyramid Module(SPM) and Semantic Feature Fusion Module(SFFM). To compensate for the weaknesses of FPN, the semantic segmentation result is utilized as an extra information source in our architecture.By constructing a semantic pyramid based on the segmentation result and fusing it with FPN, feature maps at each level can obtain the necessary information without suffering from the dilution issue. The proposed architecture could be applied on many detectors, and non-negligible improvement could be achieved. Although this method is designed for object detection, other tasks such as instance segmentation can also largely benefit from it. The proposed method brings Faster R-CNN and Mask R-CNN with ResNet-50 as backbone both 1.8 AP improvements respectively. Furthermore, SFPN improves Cascade R-CNN with backbone ResNet-101 from 42.4 AP to 43.5 AP.

Norm Loss: An Efficient yet Effective Regularization Method for Deep Neural Networks

Theodoros Georgiou, Sebastian Schmitt, Thomas Baeck, Wei Chen, Michael Lew

Auto-TLDR; Weight Soft-Regularization with Oblique Manifold for Convolutional Neural Network Training

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Convolutional neural network training can suffer from diverse issues like exploding or vanishing gradients, scaling-based weight space symmetry and covariant-shift. In order to address these issues, researchers develop weight regularization methods and activation normalization methods. In this work we propose a weight soft-regularization method based on the Oblique manifold. The proposed method uses a loss function which pushes each weight vector to have a norm close to one, i.e. the weight matrix is smoothly steered toward the so-called Oblique manifold. We evaluate our method on the very popular CIFAR-10, CIFAR-100 and ImageNet 2012 datasets using two state-of-the-art architectures, namely the ResNet and wide-ResNet. Our method introduces negligible computational overhead and the results show that it is competitive to the state-of-the-art and in some cases superior to it. Additionally, the results are less sensitive to hyperparameter settings such as batch size and regularization factor.

Improving reliability of attention branch network by introducing uncertainty

Takuya Tsukahara, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi

Auto-TLDR; Bayesian Attention Branch Network for Convolutional Neural Networks

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Convolutional neural networks (CNNs) are being used in various fields related to image recognition and are achieving high recognition accuracy. However, most existing CNNs do not consider uncertainty in their predictions; that is, they do not account for the difficulty of prediction, and the extent to which their predictions are reliable is unclear. This problem is considered to be the cause of erroneous decisions when we use CNNs in practice. By considering the uncertainty of the prediction result, it is thought that recognition accuracy would improve, and erroneous decisions would be suppressed. We propose a Bayesian attention branch network (Bayesian ABN) that incorporates uncertainty into an attention branch network (ABN). The method incorporates a Bayesian neural network (Bayesian NN) into the ABN to account for uncertainty in the prediction result. Also, it outputs prediction results from two branches and chooses the one having the lower uncertainty. In evaluations using standard object recognition datasets, we confirmed that the proposed method improves the accuracy and reliability of CNNs.

A Close Look at Deep Learning with Small Data

Lorenzo Brigato, Luca Iocchi

Auto-TLDR; Low-Complex Neural Networks for Small Data Conditions

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In this work, we perform a wide variety of experiments with different Deep Learning architectures in small data conditions. We show that model complexity is a critical factor when only a few samples per class are available. Differently from the literature, we improve the state of the art using low complexity models. We show that standard convolutional neural networks with relatively few parameters are effective in this scenario. In many of our experiments, low complexity models outperform state-of-the-art architectures. Moreover, we propose a novel network that uses an unsupervised loss to regularize its training. Such architecture either improves the results either performs comparably well to low capacity networks. Surprisingly, experiments show that the dynamic data augmentation pipeline is not beneficial in this particular domain. Statically augmenting the dataset might be a promising research direction while dropout maintains its role as a good regularizer.

Residual Fractal Network for Single Image Super Resolution by Widening and Deepening

Jiahang Gu, Zhaowei Qu, Xiaoru Wang, Jiawang Dan, Junwei Sun

Auto-TLDR; Residual fractal convolutional network for single image super-resolution

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The architecture of the convolutional neural network (CNN) plays an important role in single image super-resolution (SISR). However, most models proposed in recent years usually transplant methods or architectures that perform well in other vision fields. Thence they do not combine the characteristics of super-resolution (SR) and ignore the key information brought by the recurring texture feature in the image. To utilize patch-recurrence in SR and the high correlation of texture, we propose a residual fractal convolutional block (RFCB) and expand its depth and width to obtain residual fractal network (RFN), which contains deep residual fractal network (DRFN) and wide residual fractal network (WRFN). RFCB is recursive with multiple branches of magnified receptive field. Through the phased feature fusion module, the network focuses on extracting high-frequency texture feature that repeatedly appear in the image. We also introduce residual in residual (RIR) structure to RFCB that enables abundant low-frequency feature feed into deeper layers and reduce the difficulties of network training. RFN is the first supervised learning method to combine the patch-recurrence characteristic in SISR into network design. Extensive experiments demonstrate that RFN outperforms state-of-the-art SISR methods in terms of both quantitative metrics and visual quality, while the amount of parameters has been greatly optimized.

Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks

Yoshitomo Matsubara, Marco Levorato

Auto-TLDR; Deep Neural Networks for Remote Object Detection Using Edge Computing

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The edge computing paradigm places compute-capable devices - edge servers - at the network edge to assist mobile devices in executing data analysis tasks. Intuitively, offloading compute-intense tasks to edge servers can reduce their execution time. However, poor conditions of the wireless channel connecting the mobile devices to the edge servers may degrade the overall capture-to-output delay achieved by edge offloading. Herein, we focus on edge computing supporting remote object detection by means of Deep Neural Networks (DNN), and develop a framework to reduce the amount of data transmitted over the wireless link. The core idea we propose builds on recent approaches splitting DNNs into sections - namely head and tail models - executed by the mobile device and edge server, respectively. The wireless link, then, is used to transport the output of the last layer of the head model to the edge server, instead of the DNN input. Most prior work focuses on classification tasks and leaves the DNN structure unaltered. Herein, we focus on DNNs for three different object detection tasks, which present a much more convoluted structure, and modify the architecture of the network to: (i) achieve in-network compression by introducing a bottleneck layer in the early layers on the head model, and (ii) prefilter pictures that do not contain objects of interest using a convolutional neural network. Results show that the proposed technique represents an effective intermediate option between local and edge computing in a parameter region where these extreme point solutions fail to provide satisfactory performance.

Rethinking of Deep Models Parameters with Respect to Data Distribution

Shitala Prasad, Dongyun Lin, Yiqun Li, Sheng Dong, Zaw Min Oo

Auto-TLDR; A progressive stepwise training strategy for deep neural networks

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The performance of deep learning models are driven by various parameters but to tune all of them every time, for every dataset, is a heuristic practice. In this paper, unlike the common practice of decaying the learning rate, we propose a step-wise training strategy where the learning rate and the batch size are tuned based on the dataset size. Here, the given dataset size is progressively increased during the training to boost the network performance without saturating the learning curve, after certain epochs. We conducted extensive experiments on multiple networks and datasets to validate the proposed training strategy. The experimental results proves our hypothesis that the learning rate, the batch size and the data size are interrelated and can improve the network accuracy if an optimal progressive stepwise training strategy is applied. The proposed strategy also the overall training computational cost is reduced.

Enhanced Feature Pyramid Network for Semantic Segmentation

Mucong Ye, Ouyang Jinpeng, Ge Chen, Jing Zhang, Xiaogang Yu

Auto-TLDR; EFPN: Enhanced Feature Pyramid Network for Semantic Segmentation

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Multi-scale feature fusion has been an effective way for improving the performance of semantic segmentation. However, current methods generally fail to consider the semantic gaps between the shallow (low-level) and deep (high-level) features and thus the fusion methods may not be optimal. In this paper, to address the issues of the semantic gap between the feature from different layers, we propose a unified framework based on the U-shape encoder-decoder architecture, named Enhanced Feature Pyramid Network (EFPN). Specifically, the semantic enhancement module (SEM), boundary extraction module (BEM), and context aggregation model (CAM) are incorporated into the decoder network to improve the robustness of the multi-level features aggregation. In addition, a global fusion model (GFM) in encoder branch is proposed to capture more semantic information in the deep layers and effectively transmit the high-level semantic features to each layer. Extensive experiments are conducted and the results show that the proposed framework achieves the state-of-the-art results on three public datasets, namely PASCAL VOC 2012, Cityscapes, and PASCAL Context. Furthermore, we also demonstrate that the proposed method is effective for other visual tasks that require frequent fusing features and upsampling.

Smart Inference for Multidigit Convolutional Neural Network Based Barcode Decoding

Duy-Thao Do, Tolcha Yalew, Tae Joon Jun, Daeyoung Kim

Auto-TLDR; Smart Inference for Barcode Decoding using Deep Convolutional Neural Network

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Barcodes are ubiquitous and have been used in most of critical daily activities for decades. However, most of traditional decoders require well-founded barcode under a relatively standard condition. While wilder conditioned barcodes such as underexposed, occluded, blurry, wrinkled and rotated are commonly captured in reality, those traditional decoders show weakness of recognizing. Several works attempted to solve those challenging barcodes, but many limitations still exist. This work aims to solve the decoding problem using deep convolutional neural network with the possibility of running on portable devices. Firstly, we proposed a special modification of inference based on the feature of having checksum and test-time augmentation, named as Smart Inference (SI) in prediction phase of a trained model. SI considerably boosts accuracy and reduces the false prediction for trained models. Secondly, we have created a large practical evaluation dataset of real captured 1D barcode under various challenging conditions to test our methods vigorously, which is publicly available for other researchers. The experiments' results demonstrated the SI effectiveness with the highest accuracy of 95.85% which outperformed many existing decoders on the evaluation set. Finally, we successfully minimized the best model by knowledge distillation to a shallow model which is shown to have high accuracy (90.85%) with good inference speed of 34.2 ms per image on a real edge device.

Object Detection Model Based on Scene-Level Region Proposal Self-Attention

Yu Quan, Zhixin Li, Canlong Zhang, Huifang Ma

Auto-TLDR; Exploiting Semantic Informations for Object Detection

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The improvement of object detection performance is mostly focused on the extraction of local information near the region of interest in the image, which results in detection performance in this area being unable to achieve the desired effect. First, a depth-wise separable convolution network(D_SCNet-127 R-CNN) is built on the backbone network. Considering the importance of scene and semantic informations for visual recognition, the feature map is sent into the branch of the semantic segmentation module, region proposal network module, and the region proposal self-attention module to build the network of scene-level and region proposal self-attention module. Second, a deep reinforcement learning was utilized to achieve accurate positioning of border regression, and the calculation speed of the whole model was improved through implementing a light-weight head network. This model can effectively solve the limitation of feature extraction in traditional object detection and obtain more comprehensive detailed features. The experimental verification on MSCOCO17, VOC12, and Cityscapes datasets shows that the proposed method has good validity and scalability.

Adaptive Noise Injection for Training Stochastic Student Networks from Deterministic Teachers

Yi Xiang Marcus Tan, Yuval Elovici, Alexander Binder

Auto-TLDR; Adaptive Stochastic Networks for Adversarial Attacks

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Adversarial attacks have been a prevalent problem causing misclassification in machine learning models, with stochasticity being a promising direction towards greater robustness. However, stochastic networks frequently underperform compared to deterministic deep networks. In this work, we present a conceptually clear adaptive noise injection mechanism in combination with teacher-initialisation, which adjusts its degree of randomness dynamically through the computation of mini-batch statistics. This mechanism is embedded within a simple framework to obtain stochastic networks from existing deterministic networks. Our experiments show that our method is able to outperform prior baselines under white-box settings, exemplified through CIFAR-10 and CIFAR-100. Following which, we perform in-depth analysis on varying different components of training with our approach on the effects of robustness and accuracy, through the study of the evolution of decision boundary and trend curves of clean accuracy/attack success over differing degrees of stochasticity. We also shed light on the effects of adversarial training on a pre-trained network, through the lens of decision boundaries.

Is the Meta-Learning Idea Able to Improve the Generalization of Deep Neural Networks on the Standard Supervised Learning?

Xiang Deng, Zhongfei Zhang

Auto-TLDR; Meta-learning Based Training of Deep Neural Networks for Few-Shot Learning

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Substantial efforts have been made on improving the generalization abilities of deep neural networks (DNNs) in order to obtain better performances without introducing more parameters. On the other hand, meta-learning approaches exhibit powerful generalization on new tasks in few-shot learning. Intuitively, few-shot learning is more challenging than the standard supervised learning as each target class only has a very few or no training samples. The natural question that arises is whether the meta-learning idea can be used for improving the generalization of DNNs on the standard supervised learning. In this paper, we propose a novel meta-learning based training procedure (MLTP) for DNNs and demonstrate that the meta-learning idea can indeed improve the generalization abilities of DNNs. MLTP simulates the meta-training process by considering a batch of training samples as a task. The key idea is that the gradient descent step for improving the current task performance should also improve a new task performance, which is ignored by the current standard procedure for training neural networks. MLTP also benefits from all the existing training techniques such as dropout, weight decay, and batch normalization. We evaluate MLTP by training a variety of small and large neural networks on three benchmark datasets, i.e., CIFAR-10, CIFAR-100, and Tiny ImageNet. The experimental results show a consistently improved generalization performance on all the DNNs with different sizes, which verifies the promise of MLTP and demonstrates that the meta-learning idea is indeed able to improve the generalization of DNNs on the standard supervised learning.

Attentive Hybrid Feature Based a Two-Step Fusion for Facial Expression Recognition

Jun Weng, Yang Yang, Zichang Tan, Zhen Lei

Auto-TLDR; Attentive Hybrid Architecture for Facial Expression Recognition

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Facial expression recognition is inherently a challenging task, especially for the in-the-wild images with various occlusions and large pose variations, which may lead to the loss of some crucial information. To address it, in this paper, we propose an attentive hybrid architecture (AHA) which learns global, local and integrated features based on different face regions. Compared with one type of feature, our extracted features own complementary information and can reduce the loss of crucial information. Specifically, AHA contains three branches, where all sub-networks in those branches employ the attention mechanism to further localize the interested pixels/regions. Moreover, we propose a two-step fusion strategy based on LSTM to deeply explore the hidden correlations among different face regions. Extensive experiments on four popular expression databases (i.e., CK+, FER-2013, SFEW 2.0, RAF-DB) show the effectiveness of the proposed method.

MaxDropout: Deep Neural Network Regularization Based on Maximum Output Values

Claudio Filipi Gonçalves Santos, Danilo Colombo, Mateus Roder, Joao Paulo Papa

Auto-TLDR; MaxDropout: A Regularizer for Deep Neural Networks

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Different techniques have emerged in the deep learning scenario, such as Convolutional Neural Networks, Deep Belief Networks, and Long Short-Term Memory Networks, to cite a few. In lockstep, regularization methods, which aim to prevent overfitting by penalizing the weight connections, or turning off some units, have been widely studied either. In this paper, we present a novel approach called MaxDropout, a regularizer for deep neural network models that works in a supervised fashion by removing (shutting off) the prominent neurons (i.e., most active) in each hidden layer. The model forces fewer activated units to learn more representative information, thus providing sparsity. Regarding the experiments, we show that it is possible to improve existing neural networks and provide better results in neural networks when Dropout is replaced by MaxDropout. The proposed method was evaluated in image classification, achieving comparable results to existing regularizers, such as Cutout and RandomErasing, also improving the accuracy of neural networks that uses Dropout by replacing the existing layer by MaxDropout.

P2 Net: Augmented Parallel-Pyramid Net for Attention Guided Pose Estimation

Luanxuan Hou, Jie Cao, Yuan Zhao, Haifeng Shen, Jian Tang, Ran He

Auto-TLDR; Parallel-Pyramid Net with Partial Attention for Human Pose Estimation

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The target of human pose estimation is to determine the body parts and joint locations of persons in the image. Angular changes, motion blur and occlusion etc. in the natural scenes make this task challenging, while some joints are more difficult to be detected than others. In this paper, we propose an augmented Parallel-Pyramid Net (P^2Net) with an partial attention module. During data preprocessing, we proposed a differentiable auto data augmentation (DA^2) method in which sequences of data augmentations are formulated as a trainable and operational Convolution Neural Network (CNN) component. DA^2 improves the training efficiency and effectiveness. A parallel pyramid structure is followed to compensate the information loss introduced by the network. For the information loss problem in the backbone network, we optimize the backbone network by adopting a new parallel structure without increasing the overall computational complexity. To further refine the predictions after completion of global predictions, an Partial Attention Module (PAM) is defined to extract weighted features from different scale feature maps generated by the parallel pyramid structure. Compared with the traditional up-sampling refining, PAM can better capture the relationship between channels. Experiments corroborate the effectiveness of our proposed method. Notably, our method achieves the best performance on the challenging MSCOCO and MPII datasets.

Real-Time Monocular Depth Estimation with Extremely Light-Weight Neural Network

Mian Jhong Chiu, Wei-Chen Chiu, Hua-Tsung Chen, Jen-Hui Chuang

Auto-TLDR; Real-Time Light-Weight Depth Prediction for Obstacle Avoidance and Environment Sensing with Deep Learning-based CNN

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Obstacle avoidance and environment sensing are crucial applications in autonomous driving and robotics. Among all types of sensors, RGB camera is widely used in these applications as it can offer rich visual contents with relatively low-cost, and using a single image to perform depth estimation has become one of the main focuses in resent research works. However, prior works usually rely on highly complicated computation and power-consuming GPU to achieve such task; therefore, we focus on developing a real-time light-weight system for depth prediction in this paper. Based on the well-known encoder-decoder architecture, we propose a supervised learning-based CNN with detachable decoders that produce depth predictions with different scales. We also formulate a novel log-depth loss function that computes the difference of predicted depth map and ground truth depth map in log space, so as to increase the prediction accuracy for nearby locations. To train our model efficiently, we generate depth map and semantic segmentation with complex teacher models. Via a series of ablation studies and experiments, it is validated that our model can efficiently performs real-time depth prediction with only 0.32M parameters, with the best trained model outperforms previous works on KITTI dataset for various evaluation matrices.

PSDNet: A Balanced Architecture of Accuracy and Parameters for Semantic Segmentation

Yue Liu, Zhichao Lian

Auto-TLDR; Pyramid Pooling Module with SE1Cblock and D2SUpsample Network (PSDNet)

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Abstract—In this paper, we present our Pyramid Pooling Module （PPM） with SE1Cblock and D2SUpsample Network (PSDNet), a novel architecture for accurate semantic segmentation. Started from the known work called Pyramid Scene Parsing Network (PSPNet), PSDNet takes advantage of pyramid pooling structure with channel attention module and feature transform module in Pyramid Pooling Module (PPM). The enhanced PPM with these two components can strengthen context information flowing in the network instead of damaging it. The channel attention module we mentioned is an improved “Squeeze and Excitation with 1D Convolution” (SE1C) block which can explicitly model interrelationship between channels with fewer number of parameters. We propose a feature transform module named “Depth to Space Upsampling” (D2SUpsample) in the PPM which keeps integrity of features by transforming features while interpolating features, at the same time reducing parameters. In addition, we introduce a joint strategy in SE1Cblock which combines two variants of global pooling without increasing parameters. Compared with PSPNet, our work achieves higher accuracy on public datasets with 73.97% mIoU and 82.89% mAcc accuracy on Cityscapes Dataset based on ResNet50 backbone.

A Discriminant Information Approach to Deep Neural Network Pruning

Zejiang Hou, Sy Kung

Auto-TLDR; Channel Pruning Using Discriminant Information and Reinforcement Learning

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Network pruning has become the de facto tool to accelerate and compress deep convolutional neural networks for mobile and edge applications. Previous works tend to perform channel selection in layer-wise manner based on predefined heuristics, without considering layer importance or systematically optimizing the pruned structure. In this work, we propose a novel channel pruning method that jointly harnesses two strategies: (1) a channel importance ranking heuristics based on the feature-maps discriminant power, (2) a searching method for optimal pruning budget allocation. For the former, we propose a Discriminant Information (DI) based channel selection algorithm. We use a small batch of training samples to compute the DI score for each channel and rank the channel importance so that channels really contributing to the feature-maps discriminant power are retained. For the latter, in order to search the optimal pruning budget allocation, we formulate a reward maximization problem to discover the layer importance and generating the pruning budget accordingly. Such reward maximization can be efficiently solved by the policy gradient algorithm in reinforcement learning, yielding our final pruned network which achieves the best accuracy-efficiency trade-off. Experiments on a variety of CNN architectures and benchmark datasets show that our proposed channel pruning methods compare favorably with previous state-of-the-art methods. On ImageNet, our pruned MobileNetV2 outperforms the previous layer-wise state-of-the-art pruning method CPLI \cite{guo2020channel} by 2\% Top-1 accuracy while reducing the FLOPs by 50\%.

Simple Multi-Resolution Representation Learning for Human Pose Estimation

Trung Tran Quang, Van Giang Nguyen, Daeyoung Kim

Auto-TLDR; Multi-resolution Heatmap Learning for Human Pose Estimation

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Human pose estimation - the process of recognizing human keypoints in a given image - is one of the most important tasks in computer vision and has a wide range of applications including movement diagnostics, surveillance, or self-driving vehicle. The accuracy of human keypoint prediction is increasingly improved thanks to the burgeoning development of deep learning. Most existing methods solved human pose estimation by generating heatmaps in which the ith heatmap indicates the location confidence of the ith keypoint. In this paper, we introduce novel network structures referred to as multi-resolution representation learning for human keypoint prediction. At different resolutions in the learning process, our networks branch off and use extra layers to learn heatmap generation. We firstly consider the architectures for generating the multi-resolution heatmaps after obtaining the lowest-resolution feature maps. Our second approach allows learning during the process of feature extraction in which the heatmaps are generated at each resolution of the feature extractor. The first and second approaches are referred to as multi-resolution heatmap learning and multi-resolution feature map learning respectively. Our architectures are simple yet effective, achieving good performance. We conducted experiments on two common benchmarks for human pose estimation: MS-COCO and MPII dataset.

TAAN: Task-Aware Attention Network for Few-Shot Classification

Zhe Wang, Li Liu, Fanzhang Li

Auto-TLDR; TAAN: Task-Aware Attention Network for Few-Shot Classification

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Few-shot classification aims to recognize unlabeled samples from unseen classes given only a few labeled samples.Current approaches of few-shot learning usually employ a metriclearning framework to learn a feature similarity comparison between a query (test) example and the few support (training) examples. However, these approaches all extract features from samples independently without looking at the entire task as a whole, and so fail to provide an enough discrimination to features. Moreover, the existing approaches lack the ability to select the most relevant features for the task at hand. In this work, we propose a novel algorithm called Task-Aware Attention Network (TAAN) to address the above problems in few-shot classification. By inserting a Task-Relevant Channel Attention Module into metric-based few-shot learners, TAAN generates channel attentions for each sample by aggregating the context of the entire support set and identifies the most relevant features for similarity comparison. The experiment demonstrates that TAAN is competitive in overall performance comparing to the recent state-of-the-art systems and improves the performance considerably over baseline systems on both mini-ImageNet and tiered-ImageNet benchmarks.

Local Attention and Global Representation Collaborating for Fine-Grained Classification

He Zhang, Yunming Bai, Hui Zhang, Jing Liu, Xingguang Li, Zhaofeng He

Auto-TLDR; Weighted Region Network for Cosmetic Contact Lenses Detection

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The cosmetic contact lenses over an iris may change its original textural pattern that is the foundation for iris recognition, making the cosmetic lenses a possible and easy-to-use iris presentation attack means. Aiming at cosmetic contact lenses detection of practical application system, some approaches have been proposed but still facing unsolved problems, such as low quality iris images and inaccurate localized iris boundaries. In this paper, we propose a novel framework called Weighted Region Network (WRN) for the cosmetic contact lenses detection. The WRN includes both the local attention Weight Network and the global classification Region Network. With the inherent attention mechanism, the proposed network is able to find the most discriminative regions, which reduces the requirement for target detection and improves the ability of classification based on some specific areas and patterns. The Weight Network can be trained by using Rank loss and MSE loss without manual discriminative region annotations. Experiments are conducted on several databases and a new collected low-quality iris image database. The proposed method outperforms state-of-the-art fake iris detection algorithms, and is also effective for the fine-grained image classification task.

UHRSNet: A Semantic Segmentation Network Specifically for Ultra-High-Resolution Images

Lianlei Shan, Weiqiang Wang

Auto-TLDR; Ultra-High-Resolution Segmentation with Local and Global Feature Fusion

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Abstract—Semantic segmentation is a basic task in computer vision, but only limited attention has been devoted to the ultra-high-resolution (UHR) image segmentation. Since UHR images occupy too much memory, they cannot be directly put into GPU for training. Previous methods are cropping images to small patches or downsampling the whole images. Cropping and downsampling cause the loss of contexts and details, which is essential for segmentation accuracy. To solve this problem, we improve and simplify the local and global feature fusion method in previous works. Local features are extracted from patches and global features are from downsampled images. Meanwhile, we propose one new fusion called local feature fusion for the first time, which can make patches get information from surrounding patches. We call the network with these two fusions ultra-high-resolution segmentation network (UHRSNet). These two fusions can effectively and efficiently solve the problem caused by cropping and downsampling. Experiments show a remarkable improvement on Deepglobe dataset.