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.

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Dual-Attention Guided Dropblock Module for Weakly Supervised Object Localization

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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.

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.

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

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

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

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

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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-Wise Dense Connection Graph Convolutional Network for Skeleton-Based Action Recognition

Michael Lao Banteng, Zhiyong Wu

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Auto-TLDR; Two-stream channel-wise dense connection GCN for human action recognition

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Skeleton-based action recognition task has drawn much attention for many years. Graph Convolutional Network (GCN) has proved its effectiveness in this task. However, how to improve the model's robustness to different human actions and how to make effective use of features produced by the network are main topics needed to be further explored. Human actions are time series sequence, meaning that temporal information is a key factor to model the representation of data. The ranges of body parts involved in small actions (e.g. raise a glass or shake head) and big actions (e.g. walking or jumping) are diverse. It's crucial for the model to generate and utilize more features that can be adaptive to a wider range of actions. Furthermore, feature channels are specific with the action class, the model needs to weigh their importance and pay attention to more related ones. To address these problems, in this work, we propose a two-stream channel-wise dense connection GCN (2s-CDGCN). Specifically, the skeleton data was extracted and processed into spatial and temporal information for better feature representation. A channel-wise attention module was used to select and emphasize the more useful features generated by the network. Moreover, to ensure maximum information flow, dense connection was introduced to the network structure, which enables the network to reuse the skeleton features and generate more information adaptive and related to different human actions. Our model has shown its ability to improve the accuracy of human action recognition task on two large datasets, NTU-RGB+D and Kinetics. Extensive evaluations were conducted to prove the effectiveness of our model.

A Grid-Based Representation for Human Action Recognition

Soufiane Lamghari, Guillaume-Alexandre Bilodeau, Nicolas Saunier

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Auto-TLDR; GRAR: Grid-based Representation for Action Recognition in Videos

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Human action recognition (HAR) in videos is a fundamental research topic in computer vision. It consists mainly in understanding actions performed by humans based on a sequence of visual observations. In recent years, HAR have witnessed significant progress, especially with the emergence of deep learning models. However, most of existing approaches for action recognition rely on information that is not always relevant for the task, and are limited in the way they fuse temporal information. In this paper, we propose a novel method for human action recognition that encodes efficiently the most discriminative appearance information of an action with explicit attention on representative pose features, into a new compact grid representation. Our GRAR (Grid-based Representation for Action Recognition) method is tested on several benchmark datasets that demonstrate that our model can accurately recognize human actions, despite intra-class appearance variations and occlusion challenges.

MFI: Multi-Range Feature Interchange for Video Action Recognition

Sikai Bai, Qi Wang, Xuelong Li

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Auto-TLDR; Multi-range Feature Interchange Network for Action Recognition in Videos

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Short-range motion features and long-range dependencies are two complementary and vital cues for action recognition in videos, but it remains unclear how to efficiently and effectively extract these two features. In this paper, we propose a novel network to capture these two features in a unified 2D framework. Specifically, we first construct a Short-range Temporal Interchange (STI) block, which contains a Channels-wise Temporal Interchange (CTI) module for encoding short-range motion features. Then a Graph-based Regional Interchange (GRI) module is built to present long-range dependencies using graph convolution. Finally, we replace original bottleneck blocks in the ResNet with STI blocks and insert several GRI modules between STI blocks, to form a Multi-range Feature Interchange (MFI) Network. Practically, extensive experiments are conducted on three action recognition datasets (i.e., Something-Something V1, HMDB51, and UCF101), which demonstrate that the proposed MFI network achieves impressive results with very limited computing cost.

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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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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.

DeepPear: Deep Pose Estimation and Action Recognition

Wen-Jiin Tsai, You-Ying Jhuang

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Auto-TLDR; Human Action Recognition Using RGB Video Using 3D Human Pose and Appearance Features

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Human action recognition has been a popular issue recently because it can be applied in many applications such as intelligent surveillance systems, human-robot interaction, and autonomous vehicle control. Human action recognition using RGB video is a challenging task because the learning of actions is easily affected by the cluttered background. To cope with this problem, the proposed method estimates 3D human poses first which can help remove the cluttered background and focus on the human body. In addition to the human poses, the proposed method also utilizes appearance features nearby the predicted joints to make our action prediction context-aware. Instead of using 3D convolutional neural networks as many action recognition approaches did, the proposed method uses a two-stream architecture that aggregates the results from skeleton-based and appearance-based approaches to do action recognition. Experimental results show that the proposed method achieved state-of-the-art performance on NTU RGB+D which is a largescale dataset for human action recognition.

Semantic Bilinear Pooling for Fine-Grained Recognition

Xinjie Li, Chun Yang, Song-Lu Chen, Chao Zhu, Xu-Cheng Yin

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Auto-TLDR; Semantic bilinear pooling for fine-grained recognition with hierarchical label tree

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Naturally, fine-grained recognition, e.g., vehicle identification or bird classification, has specific hierarchical labels, where fine categories are always harder to be classified than coarse categories. However, most of the recent deep learning based methods neglect the semantic structure of fine-grained objects and do not take advantage of the traditional fine-grained recognition techniques (e.g. coarse-to-fine classification). In this paper, we propose a novel framework with a two-branch network (coarse branch and fine branch), i.e., semantic bilinear pooling, for fine-grained recognition with a hierarchical label tree. This framework can adaptively learn the semantic information from the hierarchical levels. Specifically, we design a generalized cross-entropy loss for the training of the proposed framework to fully exploit the semantic priors via considering the relevance between adjacent levels and enlarge the distance between samples of different coarse classes. Furthermore, our method leverages only the fine branch when testing so that it adds no overhead to the testing time. Experimental results show that our proposed method achieves state-of-the-art performance on four public datasets.

A Novel Region of Interest Extraction Layer for Instance Segmentation

Leonardo Rossi, Akbar Karimi, Andrea Prati

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Auto-TLDR; Generic RoI Extractor for Two-Stage Neural Network for Instance Segmentation

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Given the wide diffusion of deep neural network architectures for computer vision tasks, several new applications are nowadays more and more feasible. Among them, a particular attention has been recently given to instance segmentation, by exploiting the results achievable by two-stage networks (such as Mask R-CNN or Faster R-CNN), derived from R-CNN. In these complex architectures, a crucial role is played by the Region of Interest (RoI) extraction layer, devoted to extract a coherent subset of features from a single Feature Pyramid Network (FPN) layer attached on top of a backbone. This paper is motivated by the need to overcome to the limitations of existing RoI extractors which select only one (the best) layer from FPN. Our intuition is that all the layers of FPN retain useful information. Therefore, the proposed layer (called Generic RoI Extractor - GRoIE) introduces non-local building blocks and attention mechanisms to boost the performance. A comprehensive ablation study at component level is conducted to find the best set of algorithms and parameters for the GRoIE layer. Moreover, GRoIE can be integrated seamlessly with every two-stage architecture for both object detection and instance segmentation tasks. Therefore, the improvements brought by the use of GRoIE in different state-of-the-art architectures are also evaluated. The proposed layer leads up to gain a 1.1% AP on bounding box detection and 1.7% AP on instance segmentation. The code is publicly available on GitHub repository at https://github.com/IMPLabUniPr/mmdetection-groie

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

Jun Weng, Yang Yang, Zichang Tan, Zhen Lei

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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.

SCA Net: Sparse Channel Attention Module for Action Recognition

Hang Song, Yonghong Song, Yuanlin Zhang

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Auto-TLDR; SCA Net: Efficient Group Convolution for Sparse Channel Attention

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Channel attention has shown its great performance recently when it was incorporated into deep convolutional neural networks. However, existing methods usually require extensive computing resources due to their involuted structure, which is hard for 3D CNNs to take full advantage of. In this paper, a lightweight sparse channel attention (SCA) module implemented by efficient group convolution is proposed, which adopts the idea of sparse channel connection and involves much less parameters but brings clear performance gain. Meanwhile, to solve the lack of local channel interaction brought by group convolution, a dominant function called Aggregate-Shuffle-Diverge (ASD) is leveraged to enhance information flow over each group with no additional parameters. We also adjust the existing mainstream 3D CNNs by employing 3D convolution factorization, so as to further reduce the parameters. Our SCA module can be flexibly incorporated into most existing 3D CNNs, all of which can achieve a perfect trade-off between performance and complexity on action recognition task with factorized I3D or 3D ResNet backbone networks. The experimental results also indicate that the resulting network, namely, SCA Net can achieve an outstanding performance on UCF-101 and HMDB-51 datasets.

Arbitrary Style Transfer with Parallel Self-Attention

Tiange Zhang, Ying Gao, Feng Gao, Lin Qi, Junyu Dong

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Auto-TLDR; Self-Attention-Based Arbitrary Style Transfer Using Adaptive Instance Normalization

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Neural style transfer aims to create artistic images by synthesizing patterns from a given style image. Recently, the Adaptive Instance Normalization (AdaIN) layer is proposed to achieve real-time arbitrary style transfer. However, we observed that if crucial features based on AdaIN can be further emphasized during transfer, both content and style information will be better reflected in stylized images. Furthermore, it is always essential to preserve more details and reduce unexpected artifacts in order to generate appealing results. In this paper, we introduce an improved arbitrary style transfer method based on the self-attention mechanism. A self-attention module is designed to learn what and where to emphasize in the input image. In addition, an extra Laplacian loss is applied to preserve structure details of the content while eliminating artifacts. Experimental results demonstrate that the proposed method outperforms AdaIN and can generate more appealing results.

Attention As Activation

Yimian Dai, Stefan Oehmcke, Fabian Gieseke, Yiquan Wu, Kobus Barnard

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Auto-TLDR; Attentional Activation Units for Convolutional Networks

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Activation functions and attention mechanisms are typically treated as having different purposes and have evolved differently. However, both concepts can be formulated as a non-linear gating function. Inspired by their similarity, we propose a novel type of activation units called attentional activation~(ATAC) units as a unification of activation functions and attention mechanisms. In particular, we propose a local channel attention module for the simultaneous non-linear activation and element-wise feature refinement, which locally aggregates point-wise cross-channel feature contexts. By replacing the well-known rectified linear units by such ATAC units in convolutional networks, we can construct fully attentional networks that perform significantly better with a modest number of additional parameters. We conducted detailed ablation studies on the ATAC units using several host networks with varying network depths to empirically verify the effectiveness and efficiency of the units. Furthermore, we compared the performance of the ATAC units against existing activation functions as well as other attention mechanisms on the CIFAR-10, CIFAR-100, and ImageNet datasets. Our experimental results show that networks constructed with the proposed ATAC units generally yield performance gains over their competitors given a comparable number of parameters.

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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Second-Order Attention Guided Convolutional Activations for Visual Recognition

Shannan Chen, Qian Wang, Qiule Sun, Bin Liu, Jianxin Zhang, Qiang Zhang

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Auto-TLDR; Second-order Attention Guided Network for Convolutional Neural Networks for Visual Recognition

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Recently, modeling deep convolutional activations by the global second-order pooling has shown great advance on visual recognition tasks. However, most of the existing deep second-order statistical models mainly compute second-order statistics of activations of the last convolutional layer as image representations, and they seldom introduce second-order statistics into earlier layers to better fit network topology, thus limiting the representational ability to a certain extent. Motivated by the flexibility of attention blocks that are commonly plugged into intermediate layers of deep convolutional networks (ConvNets), this work makes an attempt to combine deep second-order statistics with attention mechanisms in ConvNets, and further proposes a novel Second-order Attention Guided Network (SoAG-Net) for visual recognition. More specifically, SoAG-Net involves several SoAG modules seemingly inserted into intermediate layers of the network, in which SoAG collects second-order statistics of convolutional activations by polynomial kernel approximation to predict channel-wise attention maps utilized for guiding the learning of convolutional activations through tensor scaling along channel dimension. SoAG improves the nonlinearity of ConvNets and enables ConvNets to fit more complicated distribution of convolutional activations. Experiment results on three commonly used datasets illuminate that SoAG-Net outperforms its counterparts and achieves competitive performance with state-of-the-art models under the same backbone.

Selective Kernel and Motion-Emphasized Loss Based Attention-Guided Network for HDR Imaging of Dynamic Scenes

Yipeng Deng, Qin Liu, Takeshi Ikenaga

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Auto-TLDR; SK-AHDRNet: A Deep Network with attention module and motion-emphasized loss function to produce ghost-free HDR images

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Ghost-like artifacts caused by ill-exposed and motion areas is one of the most challenging problems in high dynamic range (HDR) image reconstruction.When the motion range is small, previous methods based on optical flow or patch-match can suppress ghost-like artifacts by first aligning input images before merging them.However, they are not robust enough and still produce artifacts for challenging scenes where large foreground motions exist.To this end, we propose a deep network with attention module and motion-emphasized loss function to produce ghost-free HDR images. In attention module, we use channel and spatial attention to guide network to emphasize important components such as motion and saturated areas automatically. With the purpose of being robust to images with different resolutions and objects with distinct scale, we adopt the selective kernel network as the basic framework for channel attention. In addition to the attention module, the motion-emphasized loss function based on the motion and ill-exposed areas mask is designed to help network reconstruct motion areas. Experiments on the public dataset indicate that the proposed SK-AHDRNet produces ghost-free results where detail in ill-exposed areas is well recovered. The proposed method scores 43.17 with PSNR metric and 61.02 with HDR-VDP-2 metric on test which outperforms all conventional works. According to quantitative and qualitative evaluations, the proposed method can achieve state-of-the-art performance.

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.

Context-Aware Residual Module for Image Classification

Jing Bai, Ran Chen

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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.

Flow-Guided Spatial Attention Tracking for Egocentric Activity Recognition

Tianshan Liu, Kin-Man Lam

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Auto-TLDR; flow-guided spatial attention tracking for egocentric activity recognition

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The popularity of wearable cameras has opened up a new dimension for egocentric activity recognition. While some methods introduce attention mechanisms into deep learning networks to capture fine-grained hand-object interactions, they often neglect exploring the spatio-temporal relationships. Generating spatial attention, without adequately exploiting temporal consistency, will result in potentially sub-optimal performance in the video-based task. In this paper, we propose a flow-guided spatial attention tracking (F-SAT) module, which is based on enhancing motion patterns and inter-frame information, to highlight the discriminative features from regions of interest across a video sequence. A new form of input, namely the optical-flow volume, is presented to provide informative cues from moving parts for spatial attention tracking. The proposed F-SAT module is deployed to a two-branch-based deep architecture, which fuses complementary information for egocentric activity recognition. Experimental results on three egocentric activity benchmarks show that the proposed method achieves state-of-the-art performance.

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.

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.

3D Attention Mechanism for Fine-Grained Classification of Table Tennis Strokes Using a Twin Spatio-Temporal Convolutional Neural Networks

Pierre-Etienne Martin, Jenny Benois-Pineau, Renaud Péteri, Julien Morlier

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Auto-TLDR; Attentional Blocks for Action Recognition in Table Tennis Strokes

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The paper addresses the problem of recognition of actions in video with low inter-class variability such as Table Tennis strokes. Two stream, "twin" convolutional neural networks are used with 3D convolutions both on RGB data and optical flow. Actions are recognized by classification of temporal windows. We introduce 3D attention modules and examine their impact on classification efficiency. In the context of the study of sportsmen performances, a corpus of the particular actions of table tennis strokes is considered. The use of attention blocks in the network speeds up the training step and improves the classification scores up to 5% with our twin model. We visualize the impact on the obtained features and notice correlation between attention and player movements and position. Score comparison of state-of-the-art action classification method and proposed approach with attentional blocks is performed on the corpus. Proposed model with attention blocks outperforms previous model without them and our baseline.

Human-Centric Parsing Network for Human-Object Interaction Detection

Guanyu Chen, Chong Chen, Zhicheng Zhao, Fei Su

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Auto-TLDR; Human-Centric Parsing Network for Human-Object Interactions Detection

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Human-object interactions detection is an essential task of image inference, but current methods can’t efficiently make use of global knowledge in the image. To tackle this challenge, in this paper, we propose a Human-Centric Parsing Network (HCPN), which integrates global structural knowledge to infer human-object interactions. In HCPN, a semantic parse graph is first constructed by binding human-object relationships, edge features and node features, where the detected human box in image is regarded as the center node and other detected boxes are linked to it. Second, based on the message passing mechanism, edge features and node features with the relation graph are updated and finally, HCPN predicts human-object interactions and associated locations by a readout function. We evaluate our model on V-COCO dataset, and a great improvement is achieved compared with state-of-the-art methods.

Saliency Prediction on Omnidirectional Images with Brain-Like Shallow Neural Network

Zhu Dandan, Chen Yongqing, Min Xiongkuo, Zhao Defang, Zhu Yucheng, Zhou Qiangqiang, Yang Xiaokang, Tian Han

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Auto-TLDR; A Brain-like Neural Network for Saliency Prediction of Head Fixations on Omnidirectional Images

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Deep feedforward convolutional neural networks (CNNs) perform well in the saliency prediction of omnidirectional images (ODIs), and have become the leading class of candidate models of the visual processing mechanism in the primate ventral stream. These CNNs have evolved from shallow network architecture to extremely deep and branching architecture to achieve superb performance in various vision tasks, yet it is unclear how brain-like they are. In particular, these deep feedforward CNNs are difficult to mapping to ventral stream structure of the brain visual system due to their vast number of layers and missing biologically-important connections, such as recurrence. To tackle this issue, some brain-like shallow neural networks are introduced. In this paper, we propose a novel brain-like network model for saliency prediction of head fixations on ODIs. Specifically, our proposed model consists of three modules: a CORnet-S module, a template feature extraction module and a ranking attention module (RAM). The CORnet-S module is a lightweight artificial neural network (ANN) with four anatomically mapped areas (V1, V2, V4 and IT) and it can simulate the visual processing mechanism of ventral visual stream in the human brain. The template features extraction module is introduced to extract attention maps of ODIs and provide guidance for the feature ranking in the following RAM module. The RAM module is used to rank and select features that are important for fine-grained saliency prediction. Extensive experiments have validated the effectiveness of the proposed model in predicting saliency maps of ODIs, and the proposed model outperforms other state-of-the-art methods with similar scale.

Learnable Higher-Order Representation for Action Recognition

Jie Shao, Xiangyang Xue

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Auto-TLDR; Learningable Higher-Order Operations for Spatiotemporal Dynamics in Video Recognition

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Capturing spatiotemporal dynamics is an essential topic in video recognition. In this paper, we present learnable higher-order operations as a generic family of building blocks for capturing spatiotemporal dynamics from RGB input video space. Similar to higher-order functions, the weights of higher-order operations are themselves derived from the data with learnable parameters. Classical architectures such as residual learning and network-in-network are first-order operations where weights are directly learned from the data. Higher-order operations make it easier to capture context-sensitive patterns, such as motion. Self-attention models are also higher-order operations, but the attention weights are mostly computed from an affine operation or dot product. The learnable higher-order operations can be more generic and flexible. Experimentally, we show that on the task of video recognition, our higher-order models can achieve results on par with or better than the existing state-of-the-art methods on Something-Something (V1 and V2), Kinetics and Charades datasets.

RSAN: Residual Subtraction and Attention Network for Single Image Super-Resolution

Shuo Wei, Xin Sun, Haoran Zhao, Junyu Dong

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Auto-TLDR; RSAN: Residual subtraction and attention network for super-resolution

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The single-image super-resolution (SISR) aims to recover a potential high-resolution image from its low-resolution version. Recently, deep learning-based methods have played a significant role in super-resolution field due to its effectiveness and efficiency. However, most of the SISR methods neglect the importance among the feature map channels. Moreover, they can not eliminate the redundant noises, making the output image be blurred. In this paper, we propose the residual subtraction and attention network (RSAN) for powerful feature expression and channels importance learning. More specifically, RSAN firstly implements one redundance removal module to learn noise information in the feature map and subtract noise through residual learning. Then it introduces the channel attention module to amplify high-frequency information and suppress the weight of effectless channels. Experimental results on extensive public benchmarks demonstrate our RSAN achieves significant improvement over the previous SISR methods in terms of both quantitative metrics and visual quality.

Self and Channel Attention Network for Person Re-Identification

Asad Munir, Niki Martinel, Christian Micheloni

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Auto-TLDR; SCAN: Self and Channel Attention Network for Person Re-identification

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Recent research has shown promising results for person re-identification by focusing on several trends. One is designing efficient metric learning loss functions such as triplet loss family to learn the most discriminative representations. The other is learning local features by designing part based architectures to form an informative descriptor from semantically coherent parts. Some efforts adjust distant outliers to their most similar positions by using soft attention and learn the relationship between distant similar features. However, only a few prior efforts focus on channel-wise dependencies and learn non-local sharp similar part features directly for the degraded data in the person re-identification task. In this paper, we propose a novel Self and Channel Attention Network (SCAN) to model long-range dependencies between channels and feature maps. We add multiple classifiers to learn discriminative global features by using classification loss. Self Attention (SA) module and Channel Attention (CA) module are introduced to model non-local and channel-wise dependencies in the learned features. Spectral normalization is applied to the whole network to stabilize the training process. Experimental results on the person re-identification benchmarks show the proposed components achieve significant improvement with respect to the baseline.

Accurate Cell Segmentation in Digital Pathology Images Via Attention Enforced Networks

Zeyi Yao, Kaiqi Li, Guanhong Zhang, Yiwen Luo, Xiaoguang Zhou, Muyi Sun

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Auto-TLDR; AENet: Attention Enforced Network for Automatic Cell Segmentation

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Automatic cell segmentation is an essential step in the pipeline of computer-aided diagnosis (CAD), such as the detection and grading of breast cancer. Accurate segmentation of cells can not only assist the pathologists to make a more precise diagnosis, but also save much time and labor. However, this task suffers from stain variation, cell inhomogeneous intensities, background clutters and cells from different tissues. To address these issues, we propose an Attention Enforced Network (AENet), which is built on spatial attention module and channel attention module, to integrate local features with global dependencies and weight effective channels adaptively. Besides, we introduce a feature fusion branch to bridge high-level and low-level features. Finally, the marker controlled watershed algorithm is applied to post-process the predicted segmentation maps for reducing the fragmented regions. In the test stage, we present an individual color normalization method to deal with the stain variation problem. We evaluate this model on the MoNuSeg dataset. The quantitative comparisons against several prior methods demonstrate the priority of our approach.

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.

Spatial-Related and Scale-Aware Network for Crowd Counting

Lei Li, Yuan Dong, Hongliang Bai

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Auto-TLDR; Spatial Attention for Crowd Counting

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Crowd counting aims to estimate the number of people in images. Although promising progresses have been made with the prevalence of deep Convolutional Neural Networks, there still remains a challenging task due to cluttered backgrounds and varying scales of people within an image. In this paper, we propose a learnable spatial attention module which can get the spatial relations to diminish the negative impact of backgrounds. Besides, a dense hybrid dilated convolution module is also brought up to preserve information derived from varied scales. With these two modules, our network can deal with the problem caused by scale variance and background interference. To demonstrate the effectiveness of our method, we compare it with state-of-the-art algorithms on three representative crowd counting benchmarks (ShanghaiTech UCF-QNRF,UCF_CC_50). Experimental results show that our proposed network can achieve significant improvements on all the three datasets.

Self-Selective Context for Interaction Recognition

Kilickaya Kilickaya, Noureldien Hussein, Efstratios Gavves, Arnold Smeulders

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Auto-TLDR; Self-Selective Context for Human-Object Interaction Recognition

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Human-object interaction recognition aims for identifying the relationship between a human subject and an object. Researchers incorporate global scene context into the early layers of deep Convolutional Neural Networks as a solution. They report a significant increase in the performance since generally interactions are correlated with the scene (i.e. riding bicycle on the city street). However, this approach leads to the following problems. It increases the network size in the early layers, therefore not efficient. It leads to noisy filter responses when the scene is irrelevant, therefore not accurate. It only leverages scene context whereas human-object interactions offer a multitude of contexts, therefore incomplete. To circumvent these issues, in this work, we propose Self-Selective Context (SSC). SSC operates on the joint appearance of human-objects and context to bring the most discriminative context(s) into play for recognition. We devise novel contextual features that model the locality of human-object interactions and show that SSC can seamlessly integrate with the State-of-the-art interaction recognition models. Our experiments show that SSC leads to an important increase in interaction recognition performance, while using much fewer parameters.

Multi-Scale Residual Pyramid Attention Network for Monocular Depth Estimation

Jing Liu, Xiaona Zhang, Zhaoxin Li, Tianlu Mao

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Auto-TLDR; Multi-scale Residual Pyramid Attention Network for Monocular Depth Estimation

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Monocular depth estimation is a challenging problem in computer vision and is crucial for understanding 3D scene geometry. Recently, deep convolutional neural networks (DCNNs) based methods have improved the estimation accuracy significantly. However, existing methods fail to consider complex textures and geometries in scenes, thereby resulting in loss of local details, distorted object boundaries, and blurry reconstruction. In this paper, we proposed an end-to-end Multi-scale Residual Pyramid Attention Network (MRPAN) to mitigate these problems.First,we propose a Multi-scale Attention Context Aggregation (MACA) module, which consists of Spatial Attention Module (SAM) and Global Attention Module (GAM). By considering the position and scale correlation of pixels from spatial and global perspectives, the proposed module can adaptively learn the similarity between pixels so as to obtain more global context information of the image and recover the complex structure in the scene. Then we proposed an improved Residual Refinement Module (RRM) to further refine the scene structure, giving rise to deeper semantic information and retain more local details. Experimental results show that our method achieves more promisin performance in object boundaries and local details compared with other state-of-the-art methods.

Skin Lesion Classification Using Weakly-Supervised Fine-Grained Method

Xi Xue, Sei-Ichiro Kamata, Daming Luo

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Auto-TLDR; Different Region proposal module for skin lesion classification

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In recent years, skin cancer has become one of the most common cancers. Among all types of skin cancers, melanoma is the most fatal one and many people die of this disease every year. Early detection can greatly reduce the death rate and save more lives. Skin lesions are one of the early symptoms of melanoma and other types of skin cancer. So accurately recognizing various skin lesions in early stage are of great significance. There have been lots of existing works based on convolutional neural networks (CNN) to solve skin lesion classification but seldom do them involve the similarity among different lesions. For example, we find that some lesions of melanoma and nevi look similar in appearance which is hard for neural network to distinguish categories of skin lesions. Inspired by fine-grained image classification, we propose a novel network to distinguish each category accurately. In our paper, we design an effective module, distinct region proposal module (DRPM), to extract the distinct regions from each image. Spatial attention and channel-wise attention are both utilized to enrich feature maps and guide the network to focus on the highlighted areas in a weakly-supervised way. In addition, two preprocessing steps are added to ensure the network to get better results. We demonstrate the potential of the proposed method on ISIC 2017 dataset. Experiments show that our approach is effective and efficient.

A Multi-Task Neural Network for Action Recognition with 3D Key-Points

Rongxiao Tang, Wang Luyang, Zhenhua Guo

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Auto-TLDR; Multi-task Neural Network for Action Recognition and 3D Human Pose Estimation

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Action recognition and 3D human pose estimation are the fundamental problems in computer vision and closely related. In this work, we propose a multi-task neural network for action recognition and 3D human pose estimation. The results of the previous methods are still error-prone especially when tested against the images taken in-the-wild, leading error results in action recognition. To solve this problem, we propose a principled approach to generate high quality 3D pose ground truth given any in-the-wild image with a person inside. We achieve this by first devising a novel stereo inspired neural network to directly map any 2D pose to high quality 3D counterpart. Based on the high-quality 3D labels, we carefully design the multi-task framework for action recognition and 3D human pose estimation. The proposed architecture can utilize the shallow, deep features of the images, and the in-the-wild 3D human key-points to guide a more precise result. High quality 3D key-points can fully reflect the morphological features of motions, thus boosting the performance on action recognition. Experiments demonstrate that 3D pose estimation leads to significantly higher performance on action recognition than separated learning. We also evaluate the generalization ability of our method both quantitatively and qualitatively. The proposed architecture performs favorably against the baseline 3D pose estimation methods. In addition, the reported results on Penn Action and NTU datasets demonstrate the effectiveness of our method on the action recognition task.

Attention-Based Selection Strategy for Weakly Supervised Object Localization

Zhenfei Zhang

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Auto-TLDR; An Attention-based Selection Strategy for Weakly Supervised Object Localization

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Weakly Supervised Object Localization (WSOL) task aims to recognize the object position by using only image-level labels. Some previous techniques remove the most discriminative parts for all input images or random images to capture the entire object location. However, these methods can not perform the correct operation on different images such as hiding the data or feature maps that should not be hidden. In this case, both classification and localization accuracy will be affected. Meanwhile, just erasing the most important regions tends to make the model learn the less discriminative parts from outside of the objects. To address these limitations, we propose an Attention-based Selection Strategy (ASS) method to choose images that do need to be erased. Moreover, we use different threshold self-attention maps to reduce the impact of unhelpful information in one of the branches of our selection strategy. Based on our experiments, the proposed method is simple but effective to improve the performance of WSOL. In particular, ASS achieves new state-of-the-art accuracy on CUB-200-2011 dataset and works very well on ILSVRC 2016 dataset.

Real-Time Semantic Segmentation Via Region and Pixel Context Network

Yajun Li, Yazhou Liu, Quansen Sun

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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.

SAT-Net: Self-Attention and Temporal Fusion for Facial Action Unit Detection

Zhihua Li, Zheng Zhang, Lijun Yin

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Auto-TLDR; Temporal Fusion and Self-Attention Network for Facial Action Unit Detection

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Research on facial action unit detection has shown remarkable performances by using deep spatial learning models in recent years, however, it is far from reaching its full capacity in learning due to the lack of use of temporal information of AUs across time. Since the AU occurrence in one frame is highly likely related to previous frames in a temporal sequence, exploring temporal correlation of AUs across frames becomes a key motivation of this work. In this paper, we propose a novel temporal fusion and AU-supervised self-attention network (a so-called SAT-Net) to address the AU detection problem. First of all, we input the deep features of a sequence into a convolutional LSTM network and fuse the previous temporal information into the feature map of the last frame, and continue to learn the AU occurrence. Second, considering the AU detection problem is a multi-label classification problem that individual label depends only on certain facial areas, we propose a new self-learned attention mask by focusing the detection of each AU on parts of facial areas through the learning of individual attention mask for each AU, thus increasing the AU independence without the loss of any spatial relations. Our extensive experiments show that the proposed framework achieves better results of AU detection over the state-of-the-arts on two benchmark databases (BP4D and DISFA).

EDD-Net: An Efficient Defect Detection Network

Tianyu Guo, Linlin Zhang, Runwei Ding, Ge Yang

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Auto-TLDR; EfficientNet: Efficient Network for Mobile Phone Surface defect Detection

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As the most commonly used communication tool, the mobile phone has become an indispensable part of our daily life. The surface of the mobile phone as the main window of human-phone interaction directly affects the user experience. It is necessary to detect surface defects on the production line in order to ensure the high quality of the mobile phone. However, the existing mobile phone surface defect detection is mainly done manually, and currently there are few automatic defect detection methods to replace human eyes. How to quickly and accurately detect the surface defects of mobile phone is an urgent problem to be solved. Hence, an efficient defect detection network (EDD-Net) is proposed. Firstly, EfficientNet is used as the backbone network. Then, according to the small-scale of mobile phone surface defects, a feature pyramid module named GCSA-BiFPN is proposed to obtain more discriminative features. Finally, the box/class prediction network is used to achieve effective defect detection. We also build a mobile phone surface oil stain defect (MPSOSD) dataset to alleviate the lack of dataset in this field. The performance on the relevant datasets shows that the network we proposed is effective and has practical significance for industrial production.

Pose-Aware Multi-Feature Fusion Network for Driver Distraction Recognition

Mingyan Wu, Xi Zhang, Linlin Shen, Hang Yu

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Auto-TLDR; Multi-Feature Fusion Network for Distracted Driving Detection using Pose Estimation

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Traffic accidents caused by distracted driving have gradually increased in recent years. In this work, we propose a novel multi-feature fusion network based on pose estimation, for image based distracted driving detection. Since hand is the most important part of driver to infer the distracted actions, our proposed method firstly detects hands using the human body posture information. In addition to the features extracted from the whole image, our network also include the important information of hand and human body posture. The global feature, hand and pose features are finally fused by weighted combination of probability vectors and concatenation of feature maps. The experimental results show that our method achieves state-of-the-art performance on our own SZ Bus Driver dataset and the public AUC Distracted Driver dataset.

Local Attention and Global Representation Collaborating for Fine-Grained Classification

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

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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.

Face Anti-Spoofing Using Spatial Pyramid Pooling

Lei Shi, Zhuo Zhou, Zhenhua Guo

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Auto-TLDR; Spatial Pyramid Pooling for Face Anti-Spoofing

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Face recognition system is vulnerable to many kinds of presentation attacks, so how to effectively detect whether the image is from the real face is particularly important. At present, many deep learning-based anti-spoofing methods have been proposed. But these approaches have some limitations, for example, global average pooling (GAP) easily loses local information of faces, single-scale features easily ignore information differences in different scales, while a complex network is prune to be overfitting. In this paper, we propose a face anti-spoofing approach using spatial pyramid pooling (SPP). Firstly, we use ResNet-18 with a small amount of parameter as the basic model to avoid overfitting. Further, we use spatial pyramid pooling module in the single model to enhance local features while fusing multi-scale information. The effectiveness of the proposed method is evaluated on three databases, CASIA-FASD, Replay-Attack and CASIA-SURF. The experimental results show that the proposed approach can achieve state-of-the-art performance.

DARN: Deep Attentive Refinement Network for Liver Tumor Segmentation from 3D CT Volume

Yao Zhang, Jiang Tian, Cheng Zhong, Yang Zhang, Zhongchao Shi, Zhiqiang He

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Auto-TLDR; Deep Attentive Refinement Network for Liver Tumor Segmentation from 3D Computed Tomography Using Multi-Level Features

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Automatic liver tumor segmentation from 3D Computed Tomography (CT) is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning. However, accurate liver tumor segmentation remains challenging due to the large variability of tumor sizes and inhomogeneous texture. Recent advances based on Fully Convolutional Network (FCN) in liver tumor segmentation draw on success of learning discriminative multi-level features. In this paper, we propose a Deep Attentive Refinement Network (DARN) for improved liver tumor segmentation from CT volumes by fully exploiting both low and high level features embedded in different layers of FCN. Different from existing works, we exploit attention mechanism to leverage the relation of different levels of features encoded in different layers of FCN. Specifically, we introduce a Semantic Attention Refinement (SemRef) module to selectively emphasize global semantic information in low level features with the guidance of high level ones, and a Spatial Attention Refinement (SpaRef) module to adaptively enhance spatial details in high level features with the guidance of low level ones. We evaluate our network on the public MICCAI 2017 Liver Tumor Segmentation Challenge dataset (LiTS dataset) and it achieves state-of-the-art performance. The proposed refinement modules are an effective strategy to exploit multi-level features and has great potential to generalize to other medical image segmentation tasks.

Adaptive Feature Fusion Network for Gaze Tracking in Mobile Tablets

Yiwei Bao, Yihua Cheng, Yunfei Liu, Feng Lu

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Auto-TLDR; Adaptive Feature Fusion Network for Multi-stream Gaze Estimation in Mobile Tablets

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Recently, many multi-stream gaze estimation methods have been proposed. They estimate gaze from eye and face appearances and achieve reasonable accuracy. However, most of the methods simply concatenate the features extracted from eye and face appearance. The feature fusion process has been ignored. In this paper, we propose a novel Adaptive Feature Fusion Network (AFF-Net), which performs gaze tracking task in mobile tablets. We stack two-eye feature maps and utilize Squeeze-and-Excitation layers to adaptively fuse two-eye features based on different eye features. Meanwhile, we also propose Adaptive Group Normalization to recalibrate eye features with the guidance of face appearance characteristics. Extensive experiments on both GazeCapture and MPIIFaceGaze datasets demonstrate consistently superior performance of the proposed method.

Attention-Oriented Action Recognition for Real-Time Human-Robot Interaction

Ziyang Song, Ziyi Yin, Zejian Yuan, Chong Zhang, Wanchao Chi, Yonggen Ling, Shenghao Zhang

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Auto-TLDR; Attention-Oriented Multi-Level Network for Action Recognition in Interaction Scenes

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Despite the notable progress made in action recognition tasks, not much work has been done in action recognition specifically for human-robot interaction. In this paper, we deeply explore the characteristics of the action recognition task in interaction scenes and propose an attention-oriented multi-level network framework to meet the need for real-time interaction. Specifically, a Pre-Attention network is employed to roughly focus on the interactor in the scene at low resolution firstly and then perform fine-grained pose estimation at high resolution. The other compact CNN receives the extracted skeleton sequence as input for action recognition, utilizing attention-like mechanisms to capture local spatial-temporal patterns and global semantic information effectively. To evaluate our approach, we construct a new action dataset specially for the recognition task in interaction scenes. Experimental results on our dataset and high efficiency (112 fps at 640 x 480 RGBD) on the mobile computing platform (Nvidia Jetson AGX Xavier) demonstrate excellent applicability of our method on action recognition in real-time human-robot interaction.