Relation-Based Deep Attention Network with Hybrid Memory for One-Shot Person Re-Identification

One-shot person Re-identification, which owns one labeled sample among numerous unlabeled data for each identity, is proposed to tackle the problem of the shortage of labeled data. Considering the scenarios without sufficient labeled data, it is very challenging to keep abreast of the performance of the supervised task in which sufficient labeled samples are available. In this paper, we propose a relation-based attention network with hybrid memory, which can make full use of the global information to pay attention to the identity features for model training with the relation-based attention network. Importantly, our specially designed network architecture effectively reduces the interference of environmental noise. Moreover, we propose a hybrid memory to train the one-shot data and unlabeled data in a unified framework, which notably contributes to the performance of person Re-identification. In particular, our designed one-shot feature update mode effectively alleviates the problem of overfitting, which is caused by the lack of supervised information during the training process. Compared with state-of-the-art unsupervised and one-shot algorithms for person Re-identification, our method achieves considerable improvements of 6.7%, 4.6%, and 11.5% on Market-1501, DukeMTMC-reID, and MSMT17 datasets, respectively, and becomes the new state-of-the-art method for one-shot person Re-identification.

[1]  Rongrong Ji,et al.  AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-Identification , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Jian-Huang Lai,et al.  Supplementary Material for “Unsupervised Person Re-identification by Soft Multilabel Learning” , 2019 .

[3]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[4]  Longhui Wei,et al.  Person Transfer GAN to Bridge Domain Gap for Person Re-identification , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

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

[6]  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).

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

[8]  Cuiling Lan,et al.  Relation-Aware Global Attention for Person Re-Identification , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Yaohua Wang,et al.  Hierarchical Clustering With Hard-Batch Triplet Loss for Person Re-Identification , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Yi Yang,et al.  Person Re-identification: Past, Present and Future , 2016, ArXiv.

[11]  Abhishek Das,et al.  Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[12]  Liang Wang,et al.  Mask-Guided Contrastive Attention Model for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[13]  Oriol Vinyals,et al.  Representation Learning with Contrastive Predictive Coding , 2018, ArXiv.

[14]  Abhinav Gupta,et al.  Non-local Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

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

[16]  Liang Zheng,et al.  Unsupervised Person Re-identification: Clustering and Fine-tuning , 2017 .

[17]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[18]  Chunhua Shen,et al.  Self-Training With Progressive Augmentation for Unsupervised Cross-Domain Person Re-Identification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

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

[20]  Yu Wu,et al.  Exploit the Unknown Gradually: One-Shot Video-Based Person Re-identification by Stepwise Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[21]  Yunchao Wei,et al.  Self-Similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-Identification , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[22]  Shaogang Gong,et al.  Harmonious Attention Network for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[23]  Yu Wu,et al.  Progressive Learning for Person Re-Identification With One Example , 2019, IEEE Transactions on Image Processing.

[24]  Yi Yang,et al.  A Bottom-Up Clustering Approach to Unsupervised Person Re-Identification , 2019, AAAI.

[25]  Francesco Solera,et al.  Performance Measures and a Data Set for Multi-target, Multi-camera Tracking , 2016, ECCV Workshops.

[26]  Mingjie Sun,et al.  Progressive Sample Mining and Representation Learning for One-Shot Person Re-identification with Adversarial Samples , 2019, Pattern Recognit..

[27]  Chenggang Yan,et al.  Unsupervised Person Re-Identification via Softened Similarity Learning , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Shiliang Zhang,et al.  Unsupervised Person Re-Identification via Multi-Label Classification , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Liang Zheng,et al.  CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions , 2020, ECCV.

[30]  Cheng Wang,et al.  Unsupervised Domain Adaptive Re-Identification: Theory and Practice , 2018, Pattern Recognit..

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

[32]  Yi Yang,et al.  Generalizing a Person Retrieval Model Hetero- and Homogeneously , 2018, ECCV.

[33]  Shengcai Liao,et al.  Unsupervised Graph Association for Person Re-Identification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[34]  Yun Fu,et al.  Residual Non-local Attention Networks for Image Restoration , 2019, ICLR.

[35]  In So Kweon,et al.  Convolutional Block Attention Module , 2018, ECCV 2018.

[36]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[37]  Shaogang Gong,et al.  Unsupervised Tracklet Person Re-Identification , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[39]  Ping Tan,et al.  Cluster Contrast for Unsupervised Person Re-Identification , 2021, ArXiv.

[40]  Hongsheng Li,et al.  Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID , 2020, NeurIPS.

[41]  Zhiming Luo,et al.  Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-Identification , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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