Unsupervised Person Re-Identification via Multi-Label Classification

The challenge of unsupervised person re-identification (ReID) lies in learning discriminative features without true labels. This paper formulates unsupervised person ReID as a multi-label classification task to progressively seek true labels. Our method starts by assigning each person image with a single-class label, then evolves to multi-label classification by leveraging the updated ReID model for label prediction. The label prediction comprises similarity computation and cycle consistency to ensure the quality of predicted labels. To boost the ReID model training efficiency in multi-label classification, we further propose the memory-based multi-label classification loss (MMCL). MMCL works with memory-based non-parametric classifier and integrates multi-label classification and single-label classification in an unified framework. Our label prediction and MMCL work iteratively and substantially boost the ReID performance. Experiments on several large-scale person ReID datasets demonstrate the superiority of our method in unsupervised person ReID. Our method also allows to use labeled person images in other domains. Under this transfer learning setting, our method also achieves state-of-the-art performance.

[1]  Shengcai Liao,et al.  Person re-identification by Local Maximal Occurrence representation and metric learning , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Cordelia Schmid,et al.  A contextual dissimilarity measure for accurate and efficient image search , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Can Yang,et al.  Unsupervised Cross-Dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[4]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[5]  Larry S. Davis,et al.  Multi-Task Learning with Low Rank Attribute Embedding for Person Re-Identification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

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

[7]  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).

[8]  Greg Mori,et al.  Learning a Deep ConvNet for Multi-Label Classification With Partial Labels , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Shiliang Zhang,et al.  Pose-Driven Deep Convolutional Model for Person Re-identification , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

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

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

[12]  Mohan S. Kankanhalli,et al.  Unsupervised Learning of View-invariant Action Representations , 2018, NeurIPS.

[13]  Min-Ling Zhang,et al.  A Review on Multi-Label Learning Algorithms , 2014, IEEE Transactions on Knowledge and Data Engineering.

[14]  Mert R. Sabuncu,et al.  Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels , 2018, NeurIPS.

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

[16]  Yu-Chiang Frank Wang,et al.  Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

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

[18]  Jian-Huang Lai,et al.  Unsupervised Person Re-Identification by Camera-Aware Similarity Consistency Learning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[19]  Yannis Avrithis,et al.  Mining on Manifolds: Metric Learning Without Labels , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[20]  Stella X. Yu,et al.  Unsupervised Feature Learning via Non-parametric Instance Discrimination , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[21]  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.

[22]  Yoshua Bengio,et al.  Hierarchical Probabilistic Neural Network Language Model , 2005, AISTATS.

[23]  Wei Li,et al.  Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[24]  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).

[25]  Guodong Ding,et al.  Dispersion based Clustering for Unsupervised Person Re-identification , 2019, BMVC.

[26]  Michael I. Jordan,et al.  Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.

[27]  Wei-Shi Zheng,et al.  Patch-Based Discriminative Feature Learning for Unsupervised Person Re-Identification , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Shiliang Zhang,et al.  Deep Attributes Driven Multi-Camera Person Re-identification , 2016, ECCV.

[29]  Qi Tian,et al.  Pose-Guided Representation Learning for Person Re-Identification , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  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).

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

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

[33]  Alexander J. Smola,et al.  Sampling Matters in Deep Embedding Learning , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[34]  Chang-Tsun Li,et al.  Multi-task Mid-level Feature Alignment Network for Unsupervised Cross-Dataset Person Re-Identification , 2018, BMVC.

[35]  Nikos Komodakis,et al.  Unsupervised Representation Learning by Predicting Image Rotations , 2018, ICLR.

[36]  Larry S. Davis,et al.  Attributes driven tracklet-to-tracklet person re-identification using latent prototypes space mapping , 2017, Pattern Recognit..

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

[38]  Qilong Wang,et al.  Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  Yi Yang,et al.  Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

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

[41]  Yinghuan Shi,et al.  A Novel Unsupervised Camera-Aware Domain Adaptation Framework for Person Re-Identification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[42]  Aapo Hyvärinen,et al.  Noise-contrastive estimation: A new estimation principle for unnormalized statistical models , 2010, AISTATS.

[43]  Yi Yang,et al.  Camera Style Adaptation for Person Re-identification , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

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