Directed Variational Cross-encoder Network for Few-Shot Multi-image Co-segmentation

Sayan Banerjee, Divakar Bhat S, Subhasis Chaudhuri, Rajbabu Velmurugan

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Auto-TLDR; Directed Variational Inference Cross Encoder for Class Agnostic Co-Segmentation of Multiple Images

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In this paper, we propose a novel framework for class agnostic co-segmentation of multiple images using comparatively smaller datasets. We have developed a novel encoder-decoder network termed as DVICE (Directed Variational Inference Cross Encoder), which learns a continuous embedding space to ensure better similarity learning. We employ a combination of the proposed variational encoder-decoder and a novel few-shot learning approach to tackle the small sample size problem in co-segmentation. Furthermore, the proposed framework does not use any semantic class labels and is entirely class agnostic. Through exhaustive experimentation using a small volume of data over multiple datasets, we have demonstrated that our approach outperforms all existing state-of-the-art techniques.

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Multiscale Attention-Based Prototypical Network for Few-Shot Semantic Segmentation

Yifei Zhang, Desire Sidibe, Olivier Morel, Fabrice Meriaudeau

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Auto-TLDR; Few-shot Semantic Segmentation with Multiscale Feature Attention

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Deep learning-based image understanding techniques require a large number of labeled images for training. Few-shot semantic segmentation, on the contrary, aims at generalizing the segmentation ability of the model to new categories given only a few labeled samples. To tackle this problem, we propose a novel prototypical network (MAPnet) with multiscale feature attention. To fully exploit the representative features of target classes, we firstly extract rich contextual information of labeled support images via a multiscale feature enhancement module. The learned prototypes from support features provide further semantic guidance on the query image. Then we adaptively integrate multiple similarity-guided probability maps by attention mechanism, yielding an optimal pixel-wise prediction. Furthermore, the proposed method was validated on the PASCAL-5i dataset in terms of 1-way N-shot evaluation. We also test the model with weak annotations, including scribble and bounding box annotations. Both the qualitative and quantitative results demonstrate the advantages of our approach over other state-of-the-art methods.

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

Zhe Wang, Li Liu, Fanzhang Li

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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.

Dual-Attention Guided Dropblock Module for Weakly Supervised Object Localization

Junhui Yin, Siqing Zhang, Dongliang Chang, Zhanyu Ma, Jun Guo

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Auto-TLDR; Dual-Attention Guided Dropblock for Weakly Supervised Object Localization

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Attention mechanisms is frequently used to learn the discriminative features for better feature representations. In this paper, we extend the attention mechanism to the task of weakly supervised object localization (WSOL) and propose the dual-attention guided dropblock module (DGDM), which aims at learning the informative and complementary visual patterns for WSOL. This module contains two key components, the channel attention guided dropout (CAGD) and the spatial attention guided dropblock (SAGD). To model channel interdependencies, the CAGD ranks the channel attentions and treats the top-k attentions with the largest magnitudes as the important ones. It also keeps some low-valued elements to increase their value if they become important during training. The SAGD can efficiently remove the most discriminative information by erasing the contiguous regions of feature maps rather than individual pixels. This guides the model to capture the less discriminative parts for classification. Furthermore, it can also distinguish the foreground objects from the background regions to alleviate the attention misdirection. Experimental results demonstrate that the proposed method achieves new state-of-the-art localization performance.

Incorporating Depth Information into Few-Shot Semantic Segmentation

Yifei Zhang, Desire Sidibe, Olivier Morel, Fabrice Meriaudeau

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Auto-TLDR; RDNet: A Deep Neural Network for Few-shot Segmentation Using Depth Information

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Few-shot segmentation presents a significant challenge for semantic scene understanding under limited supervision. Namely, this task targets at generalizing the segmentation ability of the model to new categories given a few samples. In order to obtain complete scene information, we extend the RGB-centric methods to take advantage of complementary depth information. In this paper, we propose a two-stream deep neural network based on metric learning. Our method, known as RDNet, learns class-specific prototype representations within RGB and depth embedding spaces, respectively. The learned prototypes provide effective semantic guidance on the corresponding RGB and depth query image, leading to more accurate performance. Moreover, we build a novel outdoor scene dataset, known as Cityscapes-3i, using labeled RGB images and depth images from the Cityscapes dataset. We also perform ablation studies to explore the effective use of depth information in few-shot segmentation tasks. Experiments on Cityscapes-3i show that our method achieves promising results with visual and complementary geometric cues from only a few labeled examples.

Augmented Bi-Path Network for Few-Shot Learning

Baoming Yan, Chen Zhou, Bo Zhao, Kan Guo, Yang Jiang, Xiaobo Li, Zhang Ming, Yizhou Wang

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Auto-TLDR; Augmented Bi-path Network for Few-shot Learning

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Few-shot Learning (FSL) which aims to learn from few labeled training data is becoming a popular research topic, due to the expensive labeling cost in many real-world applications. One kind of successful FSL method learns to compare the testing (query) image and training (support) image by simply concatenating the features of two images and feeding it into the neural network. However, with few labeled data in each class, the neural network has difficulty in learning or comparing the local features of two images. Such simple image-level comparison may cause serious mis-classification. To solve this problem, we propose Augmented Bi-path Network (ABNet) for learning to compare both global and local features on multi-scales. Specifically, the salient patches are extracted and embedded as the local features for every image. Then, the model learns to augment the features for better robustness. Finally, the model learns to compare global and local features separately, \emph{i.e.}, in two paths, before merging the similarities. Extensive experiments show that the proposed ABNet outperforms the state-of-the-art methods. Both quantitative and visual ablation studies are provided to verify that the proposed modules lead to more precise comparison results.

Enhanced Feature Pyramid Network for Semantic Segmentation

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

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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.

Free-Form Image Inpainting Via Contrastive Attention Network

Xin Ma, Xiaoqiang Zhou, Huaibo Huang, Zhenhua Chai, Xiaolin Wei, Ran He

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Auto-TLDR; Self-supervised Siamese inference for image inpainting

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Most deep learning based image inpainting approaches adopt autoencoder or its variants to fill missing regions in images. Encoders are usually utilized to learn powerful representational spaces, which are important for dealing with sophisticated learning tasks. Specifically, in the image inpainting task, masks with any shapes can appear anywhere in images (i.e., free-form masks) forming complex patterns. It is difficult for encoders to capture such powerful representations under this complex situation. To tackle this problem, we propose a self-supervised Siamese inference network to improve the robustness and generalization. Moreover, the restored image usually can not be harmoniously integrated into the exiting content, especially in the boundary area. To address this problem, we propose a novel Dual Attention Fusion module (DAF), which can combine both the restored and known regions in a smoother way and be inserted into decoder layers in a plug-and-play way. DAF is developed to not only adaptively rescale channel-wise features by taking interdependencies between channels into account but also force deep convolutional neural networks (CNNs) focusing more on unknown regions. In this way, the unknown region will be naturally filled from the outside to the inside. Qualitative and quantitative experiments on multiple datasets, including facial and natural datasets (i.e., Celeb-HQ, Pairs Street View, Places2 and ImageNet), demonstrate that our proposed method outperforms against state-of-the-arts in generating high-quality inpainting results.

Complementing Representation Deficiency in Few-Shot Image Classification: A Meta-Learning Approach

Xian Zhong, Cheng Gu, Wenxin Huang, Lin Li, Shuqin Chen, Chia-Wen Lin

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Auto-TLDR; Meta-learning with Complementary Representations Network for Few-Shot Learning

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Few-shot learning is a challenging problem that has attracted more and more attention recently since abundant training samples are difficult to obtain in practical applications. Meta-learning has been proposed to address this issue, which focuses on quickly adapting a predictor as a base-learner to new tasks, given limited labeled samples. However, a critical challenge for meta-learning is the representation deficiency since it is hard to discover common information from a small number of training samples or even one, as is the representation of key features from such little information. As a result, a meta-learner cannot be trained well in a high-dimensional parameter space to generalize to new tasks. Existing methods mostly resort to extracting less expressive features so as to avoid the representation deficiency. Aiming at learning better representations, we propose a meta-learning approach with complemented representations network (MCRNet) for few-shot image classification. In particular, we embed a latent space, where latent codes are reconstructed with extra representation information to complement the representation deficiency. Furthermore, the latent space is established with variational inference, collaborating well with different base-learners, and can be extended to other models. Finally, our end-to-end framework achieves the state-of-the-art performance in image classification on three standard few-shot learning datasets.

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

Yue Liu, Zhichao Lian

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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.

Progressive Scene Segmentation Based on Self-Attention Mechanism

Yunyi Pan, Yuan Gan, Kun Liu, Yan Zhang

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Auto-TLDR; Two-Stage Semantic Scene Segmentation with Self-Attention

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Semantic scene segmentation is vital for a large variety of applications as it enables understanding of 3D data. Nowadays, various approaches based upon point clouds ignore the mathematical distribution of points and treat the points equally. The methods following this direction neglect the imbalance problem of samples that naturally exists in scenes. To avoid these issues, we propose a two-stage semantic scene segmentation framework based on self-attention mechanism and achieved state-of-the-art performance on 3D scene understanding tasks. We split the whole task into two small ones which efficiently relief the sample imbalance issue. In addition, we have designed a new self-attention block which could be inserted into submanifold convolution networks to model the long-range dependencies that exists among points. The proposed network consists of an encoder and a decoder, with the spatial-wise and channel-wise attention modules inserted. The two-stage network shares a U-Net architecture and is an end-to-end trainable framework which could predict the semantic label for the scene point clouds fed into it. Experiments on standard benchmarks of 3D scenes implies that our network could perform at par or better than the existing state-of-the-art methods.

Video Semantic Segmentation Using Deep Multi-View Representation Learning

Akrem Sellami, Salvatore Tabbone

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Auto-TLDR; Deep Multi-view Representation Learning for Video Object Segmentation

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In this paper, we propose a deep learning model based on deep multi-view representation learning, to address the video object segmentation task. The proposed model emphasizes the importance of the inherent correlation between video frames and incorporates a multi-view representation learning based on deep canonically correlated autoencoders. The multi-view representation learning in our model provides an efficient mechanism for capturing inherent correlations by jointly extracting useful features and learning better representation into a joint feature space, i.e., shared representation. To increase the training data and the learning capacity, we train the proposed model with pairs of video frames, i.e., $F_{a}$ and $F_{b}$. During the segmentation phase, the deep canonically correlated autoencoders model encodes useful features by processing multiple reference frames together, which is used to detect the frequently reappearing. Our model enhances the state-of-the-art deep learning-based methods that mainly focus on learning discriminative foreground representations over appearance and motion. Experimental results over two large benchmarks demonstrate the ability of the proposed method to outperform competitive approaches and to reach good performances, in terms of semantic segmentation.

Attention Based Coupled Framework for Road and Pothole Segmentation

Shaik Masihullah, Ritu Garg, Prerana Mukherjee, Anupama Ray

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Auto-TLDR; Few Shot Learning for Road and Pothole Segmentation on KITTI and IDD

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In this paper, we propose a novel attention based coupled framework for road and pothole segmentation. In many developing countries as well as in rural areas, the drivable areas are neither well-defined, nor well-maintained. Under such circumstances, an Advance Driver Assistant System (ADAS) is needed to assess the drivable area and alert about the potholes ahead to ensure vehicle safety. Moreover, this information can also be used in structured environments for assessment and maintenance of road health. We demonstrate few shot learning approach for pothole detection to leverage accuracy even with fewer training samples. We report the exhaustive experimental results for road segmentation on KITTI and IDD datasets. We also present pothole segmentation on IDD.

Revisiting Sequence-To-Sequence Video Object Segmentation with Multi-Task Loss and Skip-Memory

Fatemeh Azimi, Benjamin Bischke, Sebastian Palacio, Federico Raue, Jörn Hees, Andreas Dengel

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Auto-TLDR; Sequence-to-Sequence Learning for Video Object Segmentation

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Video Object Segmentation (VOS) is an active research area of the visual domain. One of its fundamental sub-tasks is semi-supervised / one-shot learning: given only the segmentation mask for the first frame, the task is to provide pixel-accurate masks for the object over the rest of the sequence. Despite much progress in the last years, we noticed that many of the existing approaches lose objects in longer sequences, especially when the object is small or briefly occluded. In this work, we build upon a sequence-to-sequence approach that employs an encoder-decoder architecture together with a memory module for exploiting the sequential data. We further improve this approach by proposing a model that manipulates multi-scale spatio-temporal information using memory-equipped skip connections. Furthermore, we incorporate an auxiliary task based on distance classification which greatly enhances the quality of edges in segmentation masks. We compare our approach to the state of the art and show considerable improvement in the contour accuracy metric and the overall segmentation accuracy.

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

Jiacheng Zhang, Zhicheng Zhao, Fei Su

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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.

Boundary-Aware Graph Convolution for Semantic Segmentation

Hanzhe Hu, Jinshi Cui, Jinshi Hongbin Zha

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Auto-TLDR; Boundary-Aware Graph Convolution for Semantic Segmentation

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Recent works have made great progress in semantic segmentation by exploiting contextual information in a local or global manner with dilated convolutions, pyramid pooling or self-attention mechanism. However, few works have focused on harvesting boundary information to improve the segmentation performance. In order to enhance the feature similarity within the object and keep discrimination from other objects, we propose a boundary-aware graph convolution (BGC) module to propagate features within the object. The graph reasoning is performed among pixels of the same object apart from the boundary pixels. Based on the proposed BGC module, we further introduce the Boundary-aware Graph Convolution Network(BGCNet), which consists of two main components including a basic segmentation network and the BGC module, forming a coarse-to-fine paradigm. Specifically, the BGC module takes the coarse segmentation feature map as node features and boundary prediction to guide graph construction. After graph convolution, the reasoned feature and the input feature are fused together to get the refined feature, producing the refined segmentation result. We conduct extensive experiments on three popular semantic segmentation benchmarks including Cityscapes, PASCAL VOC 2012 and COCO Stuff, and achieve state-of-the-art performance on all three benchmarks.

Few-Shot Few-Shot Learning and the Role of Spatial Attention

Yann Lifchitz, Yannis Avrithis, Sylvaine Picard

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Auto-TLDR; Few-shot Learning with Pre-trained Classifier on Large-Scale Datasets

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Few-shot learning is often motivated by the ability of humans to learn new tasks from few examples. However, standard few-shot classification benchmarks assume that the representation is learned on a limited amount of base class data, ignoring the amount of prior knowledge that a human may have accumulated before learning new tasks. At the same time, even if a powerful representation is available, it may happen in some domain that base class data are limited or non-existent. This motivates us to study a problem where the representation is obtained from a classifier pre-trained on a large-scale dataset of a different domain, assuming no access to its training process, while the base class data are limited to few examples per class and their role is to adapt the representation to the domain at hand rather than learn from scratch. We adapt the representation in two stages, namely on the few base class data if available and on the even fewer data of new tasks. In doing so, we obtain from the pre-trained classifier a spatial attention map that allows focusing on objects and suppressing background clutter. This is important in the new problem, because when base class data are few, the network cannot learn where to focus implicitly. We also show that a pre-trained network may be easily adapted to novel classes, without meta-learning.

Few-Shot Learning Based on Metric Learning Using Class Augmentation

Susumu Matsumi, Keiichi Yamada

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Auto-TLDR; Metric Learning for Few-shot Learning

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Few-shot learning is a machine learning problem in which new categories are learned from only a few samples. One approach for few-shot learning is metric learning, which learns an embedding space in which learning is efficient for few-shot samples. In this paper, we focus on metric learning and demonstrate that the number of classes in the training data used for metric learning has a greater impact on the accuracy of few-shot learning than the number of samples per class. We propose a few-shot learning approach based on metric learning in which the number of classes in the training data for performing metric learning is increased. The number of classes is augmented by synthesizing samples of imaginary classes at a feature level from the original training data. The proposed method is evaluated on the miniImageNet dataset using the nearest neighbor method or a support vector machine as the classifier, and the effectiveness of the approach is demonstrated.

Explanation-Guided Training for Cross-Domain Few-Shot Classification

Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, Yunqing Zhao, Ngai-Man Cheung, Alexander Binder

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Auto-TLDR; Explaination-Guided Training for Cross-Domain Few-Shot Classification

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Cross-domain few-shot classification task (CD-FSC) combines few-shot classification with the requirement to generalize across domains represented by datasets. This setup faces challenges originating from the limited labeled data in each class and, additionally, from the domain shift between training and test sets. In this paper, we introduce a novel training approach for existing FSC models. It leverages on the explanation scores, obtained from existing explanation methods when applied to the predictions of FSC models, computed for intermediate feature maps of the models. Firstly, we tailor the layer-wise relevance propagation (LRP) method to explain the prediction outcomes of FSC models. Secondly, we develop a model-agnostic explanation-guided training strategy that dynamically finds and emphasizes the features which are important for the predictions. Our contribution does not target a novel explanation method but lies in a novel application of explanations for the training phase. We show that explanation-guided training effectively improves the model generalization. We observe improved accuracy for three different FSC models: RelationNet, cross attention network, and a graph neural network-based formulation, on five few-shot learning datasets: miniImagenet, CUB, Cars, Places, and Plantae.

Coarse-To-Fine Foreground Segmentation Based on Co-Occurrence Pixel-Block and Spatio-Temporal Attention Model

Xinyu Liu, Dong Liang

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Auto-TLDR; Foreground Segmentation from coarse to Fine Using Co-occurrence Pixel-Block Model for Dynamic Scene

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Foreground segmentation in dynamic scene is an important task in video surveillance. The unsupervised background subtraction method based on background statistics modeling has difficulties in updating. On the other hand, the supervised foreground segmentation method based on deep learning relies on the large-scale of accurately annotated training data, which limits its cross-scene performance. In this paper, we propose a foreground segmentation method from coarse to fine. First, a across-scenes trained Spatio-Temporal Attention Model (STAM) is used to achieve coarse segmentation, which does not require training on specific scene. Then the coarse segmentation is used as a reference to help Co-occurrence Pixel-Block Model (CPB) complete the fine segmentation, and at the same time help CPB to update its background model. This method is more flexible than those deep-learning-based methods which depends on the specific-scene training, and realizes the accurate online dynamic update of the background model. Experimental results on WallFlower and LIMU validate our method outperforms STAM, CPB and other methods of participating in comparison.

3D Medical Multi-Modal Segmentation Network Guided by Multi-Source Correlation Constraint

Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan

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Auto-TLDR; Multi-modality Segmentation with Correlation Constrained Network

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In the field of multimodal segmentation, the correlation between different modalities can be considered for improving the segmentation results. In this paper, we propose a multi-modality segmentation network with a correlation constraint. Our network includes N model-independent encoding paths with N image sources, a correlation constrain block, a feature fusion block, and a decoding path. The model-independent encoding path can capture modality-specific features from the N modalities. Since there exists a strong correlation between different modalities, we first propose a linear correlation block to learn the correlation between modalities, then a loss function is used to guide the network to learn the correlated features based on the correlation representation block. This block forces the network to learn the latent correlated features which are more relevant for segmentation. Considering that not all the features extracted from the encoders are useful for segmentation, we propose to use dual attention based fusion block to recalibrate the features along the modality and spatial paths, which can suppress less informative features and emphasize the useful ones. The fused feature representation is finally projected by the decoder to obtain the segmentation result. Our experiment results tested on BraTS-2018 dataset for brain tumor segmentation demonstrate the effectiveness of our proposed method.

A Joint Representation Learning and Feature Modeling Approach for One-Class Recognition

Pramuditha Perera, Vishal Patel

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Auto-TLDR; Combining Generative Features and One-Class Classification for Effective One-class Recognition

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One-class recognition is traditionally approached either as a representation learning problem or a feature modelling problem. In this work, we argue that both of these approaches have their own limitations; and a more effective solution can be obtained by combining the two. The proposed approach is based on the combination of a generative framework and a one-class classification method. First, we learn generative features using the one-class data with a generative framework. We augment the learned features with the corresponding reconstruction errors to obtain augmented features. Then, we qualitatively identify a suitable feature distribution that reduces the redundancy in the chosen classifier space. Finally, we force the augmented features to take the form of this distribution using an adversarial framework. We test the effectiveness of the proposed method on three one-class classification tasks and obtain state-of-the-art results.

Aggregating Object Features Based on Attention Weights for Fine-Grained Image Retrieval

Hongli Lin, Yongqi Song, Zixuan Zeng, Weisheng Wang

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Auto-TLDR; DSAW: Unsupervised Dual-selection for Fine-Grained Image Retrieval

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Object localization and local feature representation are key issues in fine-grained image retrieval. However, the existing unsupervised methods still need to be improved in these two aspects. For conquering these issues in a unified framework, a novel unsupervised scheme, named DSAW for short, is presented in this paper. Firstly, we proposed a dual-selection (DS) method, which achieves more accurate object localization by using adaptive threshold method to perform feature selection on local and global activation map in turn. Secondly, a novel and faster self-attention weights (AW) method is developed to weight local features by measuring their importance in the global context. Finally, we also evaluated the performance of the proposed method on five fine-grained image datasets and the results showed that our DSAW outperformed the existing best method.

ACRM: Attention Cascade R-CNN with Mix-NMS for Metallic Surface Defect Detection

Junting Fang, Xiaoyang Tan, Yuhui Wang

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Auto-TLDR; Attention Cascade R-CNN with Mix Non-Maximum Suppression for Robust Metal Defect Detection

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Metallic surface defect detection is of great significance in quality control for production. However, this task is very challenging due to the noise disturbance, large appearance variation, and the ambiguous definition of the defect individual. Traditional image processing methods are unable to detect the damaged region effectively and efficiently. In this paper, we propose a new defect detection method, Attention Cascade R-CNN with Mix-NMS (ACRM), to classify and locate defects robustly. Three submodules are developed to achieve this goal: 1) a lightweight attention block is introduced, which can improve the ability in capture global and local feature both in the spatial and channel dimension; 2) we firstly apply the cascade R-CNN to our task, which exploits multiple detectors to sequentially refine the detection result robustly; 3) we introduce a new method named Mix Non-Maximum Suppression (Mix-NMS), which can significantly improve its ability in filtering the redundant detection result in our task. Extensive experiments on a real industrial dataset show that ACRM achieves state-of-the-art results compared to the existing methods, demonstrating the effectiveness and robustness of our detection method.

GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Semantic Segmentation

Zhuoying Wang, Yongtao Wang, Zhi Tang, Yangyan Li, Ying Chen, Haibin Ling, Weisi Lin

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Auto-TLDR; Gated Scale-Transfer Operation for Semantic Segmentation

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Existing CNN-based methods for semantic segmentation heavily depend on multi-scale features to meet the requirements of both semantic comprehension and detail preservation. State-of-the-art segmentation networks widely exploit conventional scale-transfer operations, i.e., up-sampling and down-sampling to learn multi-scale features. In this work, we find that these operations lead to scale-confused features and suboptimal performance because they are spatial-invariant and directly transit all feature information cross scales without spatial selection. To address this issue, we propose the Gated Scale-Transfer Operation (GSTO) to properly transit spatial-filtered features to another scale. Specifically, GSTO can work either with or without extra supervision. Unsupervised GSTO is learned from the feature itself while the supervised one is guided by the supervised probability matrix. Both forms of GSTO are lightweight and plug-and-play, which can be flexibly integrated into networks or modules for learning better multi-scale features. In particular, by plugging GSTO into HRNet, we get a more powerful backbone (namely GSTO-HRNet) for pixel labeling, and it achieves new state-of-the-art results on multiple benchmarks for semantic segmentation including Cityscapes, LIP and Pascal Context, with negligible extra computational cost. Moreover, experiment results demonstrate that GSTO can also significantly boost the performance of multi-scale feature aggregation modules like PPM and ASPP.

Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting

Pongpisit Thanasutives, Ken-Ichi Fukui, Masayuki Numao, Boonserm Kijsirikul

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Auto-TLDR; M-SFANet and M-SegNet for Crowd Counting Using Multi-Scale Fusion Networks

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In this paper, we proposed two modified neural networks based on dual path multi-scale fusion networks (SFANet) and SegNet for accurate and efficient crowd counting. Inspired by SFANet, the first model, which is named M-SFANet, is attached with atrous spatial pyramid pooling (ASPP) and context-aware module (CAN). The encoder of M-SFANet is enhanced with ASPP containing parallel atrous convolutional layers with different sampling rates and hence able to extract multi-scale features of the target object and incorporate larger context. To further deal with scale variation throughout an input image, we leverage the CAN module which adaptively encodes the scales of the contextual information. The combination yields an effective model for counting in both dense and sparse crowd scenes. Based on the SFANet decoder structure, M-SFANet's decoder has dual paths, for density map and attention map generation. The second model is called M-SegNet, which is produced by replacing the bilinear upsampling in SFANet with max unpooling that is used in SegNet. This change provides a faster model while providing competitive counting performance. Designed for high-speed surveillance applications, M-SegNet has no additional multi-scale-aware module in order to not increase the complexity. Both models are encoder-decoder based architectures and are end-to-end trainable. We conduct extensive experiments on five crowd counting datasets and one vehicle counting dataset to show that these modifications yield algorithms that could improve state-of-the-art crowd counting methods.

Deeply-Fused Attentive Network for Stereo Matching

Zuliu Yang, Xindong Ai, Weida Yang, Yong Zhao, Qifei Dai, Fuchi Li

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Auto-TLDR; DF-Net: Deep Learning-based Network for Stereo Matching

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In this paper, we propose a novel learning-based network for stereo matching called DF-Net, which makes three main contributions that are experimentally shown to have practical merit. Firstly, we further increase the accuracy by using the deeply fused spatial pyramid pooling (DF-SPP) module, which can acquire the continuous multi-scale context information in both parallel and cascade manners. Secondly, we introduce channel attention block to dynamically boost the informative features. Finally, we propose a stacked encoder-decoder structure with 3D attention gate for cost regularization. More precisely, the module fuses the coding features to their next encoder-decoder structure under the supervision of attention gate with long-range skip connection, and thus exploit deep and hierarchical context information for disparity prediction. The performance on SceneFlow and KITTI datasets shows that our model is able to generate better results against several state-of-the-art algorithms.

Makeup Style Transfer on Low-Quality Images with Weighted Multi-Scale Attention

Daniel Organisciak, Edmond S. L. Ho, Shum Hubert P. H.

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Auto-TLDR; Facial Makeup Style Transfer for Low-Resolution Images Using Multi-Scale Spatial Attention

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Facial makeup style transfer is an extremely challenging sub-field of image-to-image-translation. Due to this difficulty, state-of-the-art results are mostly reliant on the Face Parsing Algorithm, which segments a face into parts in order to easily extract makeup features. However, we find that this algorithm can only work well on high-definition images where facial features can be accurately extracted. Faces in many real-world photos, such as those including a large background or multiple people, are typically of low-resolution, which considerably hinders state-of-the-art algorithms. In this paper, we propose an end-to-end holistic approach to effectively transfer makeup styles between two low-resolution images. The idea is built upon a novel weighted multi-scale spatial attention module, which identifies salient pixel regions on low-resolution images in multiple scales, and uses channel attention to determine the most effective attention map. This design provides two benefits: low-resolution images are usually blurry to different extents, so a multi-scale architecture can select the most effective convolution kernel size to implement spatial attention; makeup is applied on both a macro-level (foundation, fake tan) and a micro-level (eyeliner, lipstick) so different scales can excel in extracting different makeup features. We develop an Augmented CycleGAN network that embeds our attention modules at selected layers to most effectively transfer makeup. We test our system with the FBD data set, which consists of many low-resolution facial images, and demonstrates that it outperforms state-of-the-art methods, particularly in transferring makeup for blurry images and partially occluded images.

Learning Object Deformation and Motion Adaption for Semi-Supervised Video Object Segmentation

Xiaoyang Zheng, Xin Tan, Jianming Guo, Lizhuang Ma

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Auto-TLDR; Semi-supervised Video Object Segmentation with Mask-propagation-based Model

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We propose a novel method to solve the task of semi-supervised video object segmentation in this paper, where the mask annotation is only given at the first frame of the video sequence. A mask-propagation-based model is applied to learn the past and current information for segmentation. Besides, due to the scarcity of training data, image/mask pairs that model object deformation and shape variance are generated for the training phase. In addition, we generate the key flips between two adjacent frames for motion adaptation. The method works in an end-to-end way, without any online fine-tuning on test videos. Extensive experiments demonstrate that our method achieves competitive performance against state-of-the-art algorithms on benchmark datasets, covering cases with single object or multiple objects. We also conduct extensive ablation experiments to analyze the effectiveness of our proposed method.

Multi-Label Contrastive Focal Loss for Pedestrian Attribute Recognition

Xiaoqiang Zheng, Zhenxia Yu, Lin Chen, Fan Zhu, Shilong Wang

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Auto-TLDR; Multi-label Contrastive Focal Loss for Pedestrian Attribute Recognition

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Pedestrian Attribute Recognition (PAR) has received extensive attention during the past few years. With the advances of deep constitutional neural networks (CNNs), the performance of PAR has been significantly improved. Existing methods tend to acquire attribute-specific features by designing various complex network structures with additional modules. Such additional modules, however, dramatically increase the number of parameters. Meanwhile, the problems of class imbalance and hard attribute retrieving remain underestimated in PAR. In this paper, we explore the optimization mechanism of the training processing to account for these problems and propose a new loss function called Multi-label Contrastive Focal Loss (MCFL). This proposed MCFL emphasizes the hard and minority attributes by using a separated re-weighting mechanism for different positive and negative classes to alleviate the impact of the imbalance. MCFL is also able to enlarge the gaps between the intra-class of multi-label attributes, to force CNNs to extract more subtle discriminative features. We evaluate the proposed MCFL on three large public pedestrian datasets, including RAP, PA-100K, and PETA. The experimental results indicate that the proposed MCFL with the ResNet-50 backbone is able to outperform other state-of-the-art approaches in comparison.

An Improved Bilinear Pooling Method for Image-Based Action Recognition

Wei Wu, Jiale Yu

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Auto-TLDR; An improved bilinear pooling method for image-based action recognition

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Action recognition in still images is a challenging task because of the complexity of human motions and the variance of background in the same action category. And some actions typically occur in fine-grained categories, with little visual differences between these categories. So extracting discriminative features or modeling various semantic parts is essential for image-based action recognition. Many methods apply expensive manual annotations to learn discriminative parts information for action recognition, which may severely discourage potential applications in real life. In recent years, bilinear pooling method has shown its effectiveness for image classification due to its learning distinctive features automatically. Inspired by this model, in this paper, an improved bilinear pooling method is proposed for avoiding the shortcomings of traditional bilinear pooling methods. The previous bilinear pooling approaches contain lots of noisy background or harmful feature information, which limit their application for action recognition. In our method, the attention mechanism is introduced into hierarchical bilinear pooling framework with mask aggregation for action recognition. The proposed model can generate the distinctive and ROI-aware feature information by combining multiple attention mask maps from the channel and spatial-wise attention features. To be more specific, our method makes the network to better pay attention to discriminative region of the vital objects in an image. We verify our model on the two challenging datasets: 1) Stanford 40 action dataset and 2) our action dataset that includes 60 categories. Experimental results demonstrate the effectiveness of our approach, which is superior to the traditional and state-of-the-art methods.

Global-Local Attention Network for Semantic Segmentation in Aerial Images

Minglong Li, Lianlei Shan, Weiqiang Wang

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Auto-TLDR; GLANet: Global-Local Attention Network for Semantic Segmentation

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Errors in semantic segmentation task could be classified into two types: large area misclassification and local inaccurate boundaries. Previously attention based methods capture rich global contextual information, this is beneficial to diminish the first type of error, but local imprecision still exists. In this paper we propose Global-Local Attention Network (GLANet) with a simultaneous consideration of global context and local details. Specifically, our GLANet is composed of two branches namely global attention branch and local attention branch, and three different modules are embedded in the two branches for the purpose of modeling semantic interdependencies in spatial, channel and boundary dimensions respectively. We sum the outputs of the two branches to further improve feature representation, leading to more precise segmentation results. The proposed method achieves very competitive segmentation accuracy on two public aerial image datasets, bringing significant improvements over baseline.

Two-Level Attention-Based Fusion Learning for RGB-D Face Recognition

Hardik Uppal, Alireza Sepas-Moghaddam, Michael Greenspan, Ali Etemad

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Auto-TLDR; Fused RGB-D Facial Recognition using Attention-Aware Feature Fusion

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With recent advances in RGB-D sensing technologies as well as improvements in machine learning and fusion techniques, RGB-D facial recognition has become an active area of research. A novel attention aware method is proposed to fuse two image modalities, RGB and depth, for enhanced RGB-D facial recognition. The proposed method first extracts features from both modalities using a convolutional feature extractor. These features are then fused using a two layer attention mechanism. The first layer focuses on the fused feature maps generated by the feature extractor, exploiting the relationship between feature maps using LSTM recurrent learning. The second layer focuses on the spatial features of those maps using convolution. The training database is preprocessed and augmented through a set of geometric transformations, and the learning process is further aided using transfer learning from a pure 2D RGB image training process. Comparative evaluations demonstrate that the proposed method outperforms other state-of-the-art approaches, including both traditional and deep neural network-based methods, on the challenging CurtinFaces and IIIT-D RGB-D benchmark databases, achieving classification accuracies over 98.2% and 99.3% respectively. The proposed attention mechanism is also compared with other attention mechanisms, demonstrating more accurate results.

CT-UNet: An Improved Neural Network Based on U-Net for Building Segmentation in Remote Sensing Images

Huanran Ye, Sheng Liu, Kun Jin, Haohao Cheng

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Auto-TLDR; Context-Transfer-UNet: A UNet-based Network for Building Segmentation in Remote Sensing Images

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With the proliferation of remote sensing images, how to segment buildings more accurately in remote sensing images is a critical challenge. First, the high resolution leads to blurred boundaries in the extracted building maps. Second, the similarity between buildings and background results in intra-class inconsistency. To address these two problems, we propose an UNet-based network named Context-Transfer-UNet (CT-UNet). Specifically, we design Dense Boundary Block (DBB). Dense Block utilizes reuse mechanism to refine features and increase recognition capabilities. Boundary Block introduces the low-level spatial information to solve the fuzzy boundary problem. Then, to handle intra-class inconsistency, we construct Spatial Channel Attention Block (SCAB). It combines context space information and selects more distinguishable features from space and channel. Finally, we propose a novel loss function to enhance the purpose of loss by adding evaluation indicator. Based on our proposed CT-UNet, we achieve 85.33% mean IoU on the Inria dataset and 91.00% mean IoU on the WHU dataset, which outperforms our baseline (U-Net ResNet-34) by 3.76% and Web-Net by 2.24%.

Multi-Scale Cascading Network with Compact Feature Learning for RGB-Infrared Person Re-Identification

Can Zhang, Hong Liu, Wei Guo, Mang Ye

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Auto-TLDR; Multi-Scale Part-Aware Cascading for RGB-Infrared Person Re-identification

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RGB-Infrared person re-identification (RGB-IR Re-ID) aims to matching persons from heterogeneous images captured by visible and thermal cameras, which is of great significance in surveillance system under poor light conditions. Facing great challenges in complex variances including conventional single-modality and additional inter-modality discrepancies, most of existing RGB-IR Re-ID methods directly work on global features for simultaneous elimination, whereas modality-specific noises and modality-shared features are not well considered. To address these issues, a novel Multi-Scale Part-Aware Cascading framework (MSPAC) is formulated by aggregating multi-scale fine-grained features from part to global in a cascading manner, which results in an unified representation robust to noises. Moreover, a marginal exponential center (MeCen) loss is introduced to jointly eliminate mixed variances, which enables to model cross-modality correlations on sharable salient features. Extensive experiments are conducted for demonstration that the proposed method outperforms all the state-of-the-arts by a large margin.

Rotation Invariant Aerial Image Retrieval with Group Convolutional Metric Learning

Hyunseung Chung, Woo-Jeoung Nam, Seong-Whan Lee

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Auto-TLDR; Robust Remote Sensing Image Retrieval Using Group Convolution with Attention Mechanism and Metric Learning

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Remote sensing image retrieval (RSIR) is the process of ranking database images depending on the degree of similarity compared to the query image. As the complexity of RSIR increases due to the diversity in shooting range, angle, and location of remote sensors, there is an increasing demand for methods to address these issues and improve retrieval performance. In this work, we introduce a novel method for retrieving aerial images by merging group convolution with attention mechanism and metric learning, resulting in robustness to rotational variations. For refinement and emphasis on important features, we applied channel attention in each group convolution stage. By utilizing the characteristics of group convolution and channel-wise attention, it is possible to acknowledge the equality among rotated but identically located images. The training procedure has two main steps: (i) training the network with Aerial Image Dataset (AID) for classification, (ii) fine-tuning the network with triplet-loss for retrieval with Google Earth South Korea and NWPU-RESISC45 datasets. Results show that the proposed method performance exceeds other state-of-the-art retrieval methods in both rotated and original environments. Furthermore, we utilize class activation maps (CAM) to visualize the distinct difference of main features between our method and baseline, resulting in better adaptability in rotated environments.

Coarse to Fine: Progressive and Multi-Task Learning for Salient Object Detection

Dong-Goo Kang, Sangwoo Park, Joonki Paik

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Auto-TLDR; Progressive and mutl-task learning scheme for salient object detection

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Most deep learning-based salient object detection (SOD) methods tried to manipulate the convolution block to effectively capture the context of object. In this paper, we propose a novel method, called progressive and mutl-task learning scheme, to extract the context of object by only manipulating the learning scheme without changing the network architecture. The progressive learning scheme is a method to grow the decoder progressively in the train phase. In other words, starting from easier low-resolution layers, it gradually adds high-resolution layers. Although the progressive learning successfullyl captures the context of object, its output boundary tends to be rough. To solve this problem, we also propose a multi-task learning (MTL) scheme that processes the object saliency map and contour in a single network jointly. The proposed MTL scheme trains the network in an edge-preserved direction through an auxiliary branch that learns contours. The proposed a learning scheme can be combined with other convolution block manipulation methods. Extensive experiments on five datasets show that the proposed method performs best compared with state-of-the-art methods in most cases.

Dynamic Guided Network for Monocular Depth Estimation

Xiaoxia Xing, Yinghao Cai, Yiping Yang, Dayong Wen

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Auto-TLDR; DGNet: Dynamic Guidance Upsampling for Self-attention-Decoding for Monocular Depth Estimation

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Self-attention or encoder-decoder structure has been widely used in deep neural networks for monocular depth estimation tasks. The former mechanism are capable to capture long-range information by computing the representation of each position by a weighted sum of the features at all positions, while the latter networks can capture structural details information by gradually recovering the spatial information. In this work, we combine the advantages of both methods. Specifically, our proposed model, DGNet, extends EMANet Network by adding an effective decoder module to refine the depth results. In the decoder stage, we further design dynamic guidance upsampling which uses local neighboring information of low-level features guide coarser depth to upsample. In this way, dynamic guidance upsampling generates content-dependent and spatially-variant kernels for depth upsampling which makes full use of spatial details information from low-level features. Experimental results demonstrate that our method obtains higher accuracy and generates the desired depth map.

Do Not Treat Boundaries and Regions Differently: An Example on Heart Left Atrial Segmentation

Zhou Zhao, Elodie Puybareau, Nicolas Boutry, Thierry Geraud

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Auto-TLDR; Attention Full Convolutional Network for Atrial Segmentation using ResNet-101 Architecture

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Atrial fibrillation is the most common heart rhythm disease. Due to a lack of understanding in matter of underlying atrial structures, current treatments are still not satisfying. Recently, with the popularity of deep learning, many segmentation methods based on fully convolutional networks have been proposed to analyze atrial structures, especially from late gadolinium-enhanced magnetic resonance imaging. However, two problems still occur: 1) segmentation results include the atrial-like background; 2) boundaries are very hard to segment. Most segmentation approaches design a specific network that mainly focuses on the regions, to the detriment of the boundaries. Therefore, this paper proposes an attention full convolutional network framework based on the ResNet-101 architecture, which focuses on boundaries as much as on regions. The additional attention module is added to have the network pay more attention on regions and then to reduce the impact of the misleading similarity of neighboring tissues. We also use a hybrid loss composed of a region loss and a boundary loss to treat boundaries and regions at the same time. We demonstrate the efficiency of the proposed approach on the MICCAI 2018 Atrial Segmentation Challenge public dataset.

GuCNet: A Guided Clustering-Based Network for Improved Classification

Ushasi Chaudhuri, Syomantak Chaudhuri, Subhasis Chaudhuri

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Auto-TLDR; Semantic Classification of Challenging Dataset Using Guide Datasets

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We deal with the problem of semantic classification of challenging and highly-cluttered dataset. We present a novel, and yet a very simple classification technique by leveraging the ease of classifiability of any existing well separable dataset for guidance. Since the guide dataset which may or may not have any semantic relationship with the experimental dataset, forms well separable clusters in the feature set, the proposed network tries to embed class-wise features of the challenging dataset to those distinct clusters of the guide set, making them more separable. Depending on the availability, we propose two types of guide sets: one using texture (image) guides and another using prototype vectors representing cluster centers. Experimental results obtained on the challenging benchmark RSSCN, LSUN, and TU-Berlin datasets establish the efficacy of the proposed method as we outperform the existing state-of-the-art techniques by a considerable margin.

MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization

Yangbin Chen, Yun Ma, Tom Ko, Jianping Wang, Qing Li

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Auto-TLDR; MetaMix: A Meta-Agnostic Meta-Learning Algorithm for Few-Shot Classification

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Model-Agnostic Meta-Learning (MAML) and its variants are popular few-shot classification methods. They train an initializer across a variety of sampled learning tasks (also known as episodes) such that the initialized model can adapt quickly to new tasks. However, within each episode, current MAML-based algorithms have limitations in forming generalizable decision boundaries using only a few training examples. In this paper, we propose an approach called MetaMix. It generates virtual examples within each episode to regularize the backbone models. MetaMix can be applied in any of the MAML-based algorithms and learn the decision boundaries which are more generalizable to new tasks. Experiments on the mini-ImageNet, CUB, and FC100 datasets show that MetaMix improves the performance of MAML-based algorithms and achieves the state-of-the-art result when applied in Meta-Transfer Learning.

Attentive Part-Aware Networks for Partial Person Re-Identification

Lijuan Huo, Chunfeng Song, Zhengyi Liu, Zhaoxiang Zhang

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Auto-TLDR; Part-Aware Learning for Partial Person Re-identification

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Partial person re-identification (re-ID) refers to re-identify a person through occluded images. It suffers from two major challenges, i.e., insufficient training data and incomplete probe image. In this paper, we introduce an automatic data augmentation module and a part-aware learning method for partial re-identification. On the one hand, we adopt the data augmentation to enhance the training data and help learns more stabler partial features. On the other hand, we intuitively find that the partial person images usually have fixed percentages of parts, therefore, in partial person re-id task, the probe image could be cropped from the pictures and divided into several different partial types following fixed ratios. Based on the cropped images, we propose the Cropping Type Consistency (CTC) loss to classify the cropping types of partial images. Moreover, in order to help the network better fit the generated and cropped data, we incorporate the Block Attention Mechanism (BAM) into the framework for attentive learning. To enhance the retrieval performance in the inference stage, we implement cropping on gallery images according to the predicted types of probe partial images. Through calculating feature distances between the partial image and the cropped holistic gallery images, we can recognize the right person from the gallery. To validate the effectiveness of our approach, we conduct extensive experiments on the partial re-ID benchmarks and achieve state-of-the-art performance.

Generalized Local Attention Pooling for Deep Metric Learning

Carlos Roig Mari, David Varas, Issey Masuda, Juan Carlos Riveiro, Elisenda Bou-Balust

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Auto-TLDR; Generalized Local Attention Pooling for Deep Metric Learning

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Deep metric learning has been key to recent advances in face verification and image retrieval amongst others. These systems consist on a feature extraction block (extracts feature maps from images) followed by a spatial dimensionality reduction block (generates compact image representations from the feature maps) and an embedding generation module (projects the image representation to the embedding space). While research on deep metric learning has focused on improving the losses for the embedding generation module, the dimensionality reduction block has been overlooked. In this work, we propose a novel method to generate compact image representations which uses local spatial information through an attention mechanism, named Generalized Local Attention Pooling (GLAP). This method, instead of being placed at the end layer of the backbone, is connected at an intermediate level, resulting in lower memory requirements. We assess the performance of the aforementioned method by comparing it with multiple dimensionality reduction techniques, demonstrating the importance of using attention weights to generate robust compact image representations. Moreover, we compare the performance of multiple state-of-the-art losses using the standard deep metric learning system against the same experiment with our GLAP. Experiments showcase that the proposed Generalized Local Attention Pooling mechanism outperforms other pooling methods when compared with current state-of-the-art losses for deep metric learning.

Transitional Asymmetric Non-Local Neural Networks for Real-World Dirt Road Segmentation

Yooseung Wang, Jihun Park

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Auto-TLDR; Transitional Asymmetric Non-Local Neural Networks for Semantic Segmentation on Dirt Roads

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Understanding images by predicting pixel-level semantic classes is a fundamental task in computer vision and is one of the most important techniques for autonomous driving. Recent approaches based on deep convolutional neural networks have dramatically improved the speed and accuracy of semantic segmentation on paved road datasets, however, dirt roads have yet to be systematically studied. Dirt roads do not contain clear boundaries between drivable and non-drivable regions; and thus, this difficulty must be overcome for the realization of fully autonomous vehicles. The key idea of our approach is to apply lightweight non-local blocks to reinforce stage-wise long-range dependencies in encoder-decoder style backbone networks. Experiments on 4,687 images of a dirt road dataset show that our transitional asymmetric non-local neural networks present a higher accuracy with lower computational costs compared to state-of-the-art models.

Semantics to Space(S2S): Embedding Semantics into Spatial Space for Zero-Shot Verb-Object Query Inferencing

Sungmin Eum, Heesung Kwon

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Auto-TLDR; Semantics-to-Space: Deep Zero-Shot Learning for Verb-Object Interaction with Vectors

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We present a novel deep zero-shot learning (ZSL) model for inferencing human-object-interaction with verb-object (VO) query. While the previous two-stream ZSL approaches only use the semantic/textual information to be fed into the query stream, we seek to incorporate and embed the semantics into the visual representation stream as well. Our approach is powered by Semantics-to-Space (S2S) architecture where semantics derived from the residing objects are embedded into a spatial space of the visual stream. This architecture allows the co-capturing of the semantic attributes of the human and the objects along with their location/size/silhouette information. To validate, we have constructed a new dataset, Verb-Transferability 60 (VT60). VT60 provides 60 different VO pairs with overlapping verbs tailored for testing two-stream ZSL approaches with VO query. Experimental evaluations show that our approach not only outperforms the state-of-the-art, but also shows the capability of consistently improving performance regardless of which ZSL baseline architecture is used.

Pose-Robust Face Recognition by Deep Meta Capsule Network-Based Equivariant Embedding

Fangyu Wu, Jeremy Simon Smith, Wenjin Lu, Bailing Zhang

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Auto-TLDR; Deep Meta Capsule Network-based Equivariant Embedding Model for Pose-Robust Face Recognition

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Despite the exceptional success in face recognition related technologies, handling large pose variations still remains a key challenge. Current techniques for pose-robust face recognition either, directly extract pose-invariant features, or first synthesize a face that matches the target pose before feature extraction. It is more desirable to learn face representations equivariant to pose variations. To this end, this paper proposes a deep meta Capsule network-based Equivariant Embedding Model (DM-CEEM) with three distinct novelties. First, the proposed RB-CapsNet allows DM-CEEM to learn an equivariant embedding for pose variations and achieve the desired transformation for input face images. Second, we introduce a new version of a Capsule network called RB-CapsNet to extend CapsNet to perform a profile-to-frontal face transformation in deep feature space. Third, we train the DM-CEEM in a meta way by treating a single overall classification target as multiple sub-tasks that satisfy certain unknown probabilities. In each sub-task, we sample the support and query sets randomly. The experimental results on both controlled and in-the-wild databases demonstrate the superiority of DM-CEEM over state-of-the-art.

Image Representation Learning by Transformation Regression

Xifeng Guo, Jiyuan Liu, Sihang Zhou, En Zhu, Shihao Dong

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Auto-TLDR; Self-supervised Image Representation Learning using Continuous Parameter Prediction

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Self-supervised learning is a thriving research direction since it can relieve the burden of human labeling for machine learning by seeking for supervision from data instead of human annotation. Although demonstrating promising performance in various applications, we observe that the existing methods usually model the auxiliary learning tasks as classification tasks with finite discrete labels, leading to insufficient supervisory signals, which in turn restricts the representation quality. In this paper, to solve the above problem and make full use of the supervision from data, we design a regression model to predict the continuous parameters of a group of transformations, i.e., image rotation, translation, and scaling. Surprisingly, this naive modification stimulates tremendous potential from data and the resulting supervisory signal has largely improved the performance of image representation learning. Extensive experiments on four image datasets, including CIFAR10, CIFAR100, STL10, and SVHN, indicate that our proposed algorithm outperforms the state-of-the-art unsupervised learning methods by a large margin in terms of classification accuracy. Crucially, we find that with our proposed training mechanism as an initialization, the performance of the existing state-of-the-art classification deep architectures can be preferably improved.

Forground-Guided Vehicle Perception Framework

Kun Tian, Tong Zhou, Shiming Xiang, Chunhong Pan

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Auto-TLDR; A foreground segmentation branch for vehicle detection

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As the basis of advanced visual tasks such as vehicle tracking and traffic flow analysis, vehicle detection needs to accurately predict the position and category of vehicle objects. In the past decade, deep learning based methods have made great progress. However, we also notice that some existing cases are not studied thoroughly. First, false positive on the background regions is one of the critical problems. Second, most of the previous approaches only optimize a single vehicle detection model, ignoring the relationship between different visual perception tasks. In response to the above two findings, we introduce a foreground segmentation branch for the first time, which can predict the pixel level of vehicles in advance. Furthermore, two attention modules are designed to guide the work of the detection branch. The proposed method can be easily grafted into the one-stage and two-stage detection framework. We evaluate the effectiveness of our model on LSVH, a dataset with large variations in vehicle scales, and achieve the state-of-the-art detection accuracy.

A Prototype-Based Generalized Zero-Shot Learning Framework for Hand Gesture Recognition

Jinting Wu, Yujia Zhang, Xiao-Guang Zhao

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Auto-TLDR; Generalized Zero-Shot Learning for Hand Gesture Recognition

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Hand gesture recognition plays a significant role in human-computer interaction for understanding various human gestures and their intent. However, most prior works can only recognize gestures of limited labeled classes and fail to adapt to new categories. The task of Generalized Zero-Shot Learning (GZSL) for hand gesture recognition aims to address the above issue by leveraging semantic representations and detecting both seen and unseen class samples. In this paper, we propose an end-to-end prototype-based GZSL framework for hand gesture recognition which consists of two branches. The first branch is a prototype-based detector that learns gesture representations and determines whether an input sample belongs to a seen or unseen category. The second branch is a zero-shot label predictor which takes the features of unseen classes as input and outputs predictions through a learned mapping mechanism between the feature and the semantic space. We further establish a hand gesture dataset that specifically targets this GZSL task, and comprehensive experiments on this dataset demonstrate the effectiveness of our proposed approach on recognizingQuestionnaire both seen and unseen gestures.