Attention Deep Model With Multi-Scale Deep Supervision for Person Re-Identification

As an important part of intelligent surveillance systems, person re-identification (PReID) has drawn wide attention of the public in recent years. Many recent deep learning-based PReID methods have used attention or multi-scale feature learning modules to enhance the discrimination of the learned deep features. However, the attention mechanisms may lose some important feature information. Moreover, the multi-scale models usually embed the multi-scale feature learning module into the backbone network, which increases the complexity of testing network. To address the two issues, we propose a multi-scale deep supervision with attention feature learning deep model for PReID. Specifically, we introduce a reverse attention module to remedy the feature information losing issue caused by the attention module, and a multi-scale feature learning layer with deep supervision to train the network. The proposed modules are only used at the training phase and discarded during the test phase. Experiments on Market-1501, DukeMTMC-reID, CUHK03 and MSMT17 datasets. demonstrate that our model notably beats other competitive state-of-the-art models.

[1]  Xiaogang Wang,et al.  End-to-End Deep Kronecker-Product Matching for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[2]  D.-S. Huang,et al.  Radial Basis Probabilistic Neural Networks: Model and Application , 1999, Int. J. Pattern Recognit. Artif. Intell..

[3]  Kaiqi Huang,et al.  Towards Rich Feature Discovery With Class Activation Maps Augmentation for Person Re-Identification , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  In-So Kweon,et al.  CBAM: Convolutional Block Attention Module , 2018, ECCV.

[5]  Jianyuan Guo,et al.  Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[6]  Nanning Zheng,et al.  Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Lin Wu,et al.  PersonNet: Person Re-identification with Deep Convolutional Neural Networks , 2016, ArXiv.

[8]  Xiong Chen,et al.  Learning Discriminative Features with Multiple Granularities for Person Re-Identification , 2018, ACM Multimedia.

[9]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[11]  Yunzhou Zhang,et al.  Dual Reverse Attention Networks for Person Re-Identification , 2019, 2019 IEEE International Conference on Image Processing (ICIP).

[12]  Qi Tian,et al.  Beyond Part Models: Person Retrieval with Refined Part Pooling , 2017, ECCV.

[13]  Zuozhuo Dai,et al.  Batch DropBlock Network for Person Re-Identification and Beyond , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[14]  Xiao Qin,et al.  An effective image classification method for shallow densely connected convolution networks through squeezing and splitting techniques , 2019, Applied Intelligence.

[15]  Sergio A. Velastin,et al.  Local Fisher Discriminant Analysis for Pedestrian Re-identification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Xiaogang Wang,et al.  DeepReID: Deep Filter Pairing Neural Network for Person Re-identification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Yang Hua,et al.  Ranked List Loss for Deep Metric Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Xiao-Ping Zhang,et al.  A novel deep model with multi-loss and efficient training for person re-identification , 2019, Neurocomputing.

[19]  Jian Yang,et al.  Selective Kernel Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Xiang Li,et al.  An enhanced deep feature representation for person re-identification , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

[22]  Lucas Beyer,et al.  In Defense of the Triplet Loss for Person Re-Identification , 2017, ArXiv.

[23]  Muhittin Gokmen,et al.  Human Semantic Parsing for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[24]  Kim-Hui Yap,et al.  AANet: Attribute Attention Network for Person Re-Identifications , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Xiaogang Wang,et al.  Learning Mid-level Filters for Person Re-identification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Shiguang Shan,et al.  Interaction-And-Aggregation Network for Person Re-Identification , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Wei Jiang,et al.  Bag of Tricks and a Strong Baseline for Deep Person Re-Identification , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[28]  Michael Jones,et al.  An improved deep learning architecture for person re-identification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Yi Yang,et al.  Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[30]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Lars Petersson,et al.  Bilinear Attention Networks for Person Retrieval , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[32]  Tao Xiang,et al.  Pose-Normalized Image Generation for Person Re-identification , 2017, ECCV.

[33]  Xiaogang Wang,et al.  Person Re-identification with Deep Similarity-Guided Graph Neural Network , 2018, ECCV.

[34]  Yudong Zhang,et al.  Cerebral micro‐bleeding identification based on a nine‐layer convolutional neural network with stochastic pooling , 2019, Concurr. Comput. Pract. Exp..

[35]  Amit K. Roy-Chowdhury,et al.  Temporal Model Adaptation for Person Re-identification , 2016, ECCV.

[36]  Nicu Sebe,et al.  Group Consistent Similarity Learning via Deep CRF for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[37]  De-Shuang Huang,et al.  Omnidirectional Feature Learning for Person Re-Identification , 2019, IEEE Access.

[38]  Qi Tian,et al.  Scalable Person Re-identification: A Benchmark , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[39]  Tao Xiang,et al.  Leader-Based Multi-Scale Attention Deep Architecture for Person Re-Identification , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Hai Tao,et al.  Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features , 2008, ECCV.

[41]  Xiao-Ping Zhang,et al.  Deep learning-based methods for person re-identification: A comprehensive review , 2019, Neurocomputing.

[42]  Zhedong Zheng,et al.  Joint Discriminative and Generative Learning for Person Re-Identification , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[43]  De-Shuang Huang,et al.  A Constructive Hybrid Structure Optimization Methodology for Radial Basis Probabilistic Neural Networks , 2008, IEEE Transactions on Neural Networks.

[44]  Cheng Wang,et al.  Mancs: A Multi-task Attentional Network with Curriculum Sampling for Person Re-Identification , 2018, ECCV.

[45]  Weihong Deng,et al.  Mixed High-Order Attention Network for Person Re-Identification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[46]  Liang Lin,et al.  Deep feature learning with relative distance comparison for person re-identification , 2015, Pattern Recognit..

[47]  Arun Kumar Sangaiah,et al.  Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization , 2018, Neural Computing and Applications.

[48]  Xiaogang Wang,et al.  FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification , 2018, NeurIPS.

[49]  Liang Zheng,et al.  Improving Person Re-identification by Attribute and Identity Learning , 2017, Pattern Recognit..

[50]  Tao Mei,et al.  Part-Aligned Bilinear Representations for Person Re-identification , 2018, ECCV.

[51]  Di Wu,et al.  A deep model with combined losses for person re-identification , 2019, Cognitive Systems Research.

[52]  Xiaogang Wang,et al.  Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[53]  Bin Liu,et al.  Unilateral sensorineural hearing loss identification based on double-density dual-tree complex wavelet transform and multinomial logistic regression , 2019, Integr. Comput. Aided Eng..

[54]  In-So Kweon,et al.  BAM: Bottleneck Attention Module , 2018, BMVC.

[55]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[56]  Yu Wu,et al.  Auto-ReID: Searching for a Part-Aware ConvNet for Person Re-Identification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[57]  Jinxing Cheng,et al.  Multi-Scale Body-Part Mask Guided Attention for Person Re-Identification , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[58]  Yingli Tian,et al.  A Heuristic Neural Network Structure Relying on Fuzzy Logic for Images Scoring , 2020, IEEE Transactions on Fuzzy Systems.

[59]  Ziyan Wu,et al.  Re-Identification With Consistent Attentive Siamese Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[60]  Di Wu,et al.  Omni-directional Feature Learning for Person Re-identification , 2018, ArXiv.

[61]  Liang Zheng,et al.  Re-ranking Person Re-identification with k-Reciprocal Encoding , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[62]  Frédéric Jurie,et al.  PCCA: A new approach for distance learning from sparse pairwise constraints , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[63]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[64]  Yan Wang,et al.  Resource Aware Person Re-identification Across Multiple Resolutions , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[65]  M. Saquib Sarfraz,et al.  A Pose-Sensitive Embedding for Person Re-identification with Expanded Cross Neighborhood Re-ranking , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[66]  Yi Yang,et al.  Random Erasing Data Augmentation , 2017, AAAI.

[67]  Dong Liu,et al.  Multi-Scale Triplet CNN for Person Re-Identification , 2016, ACM Multimedia.

[68]  Andrea Cavallaro,et al.  Omni-Scale Feature Learning for Person Re-Identification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[69]  Shaogang Gong,et al.  Person Re-identification by Deep Learning Multi-scale Representations , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[70]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[71]  Narendra Ahuja,et al.  Pedestrian Recognition with a Learned Metric , 2010, ACCV.

[72]  Xiaogang Wang,et al.  Deep Group-Shuffling Random Walk for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.