Multi-task Mid-level Feature Alignment Network for Unsupervised Cross-Dataset Person Re-Identification

Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training. Such a setting severely limits their scalability in real-world applications where no labelled samples are available during the training phase. To overcome this limitation, we develop a novel unsupervised Multi-task Mid-level Feature Alignment (MMFA) network for the unsupervised cross-dataset person re-identification task. Under the assumption that the source and target datasets share the same set of mid-level semantic attributes, our proposed model can be jointly optimised under the person's identity classification and the attribute learning task with a cross-dataset mid-level feature alignment regularisation term. In this way, the learned feature representation can be better generalised from one dataset to another which further improve the person re-identification accuracy. Experimental results on four benchmark datasets demonstrate that our proposed method outperforms the state-of-the-art baselines.

[1]  Tao Xiang,et al.  Unsupervised Learning of Generative Topic Saliency for Person Re-identification , 2014, BMVC.

[2]  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).

[3]  Michael I. Jordan,et al.  Deep Transfer Learning with Joint Adaptation Networks , 2016, ICML.

[4]  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.

[5]  Shaogang Gong,et al.  Unsupervised Cross-Dataset Transfer Learning for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[7]  Alberto Del Bimbo,et al.  Person Re-Identification by Iterative Re-Weighted Sparse Ranking , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[9]  Wei Zeng,et al.  Exploiting Multi-grain Ranking Constraints for Precisely Searching Visually-similar Vehicles , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[10]  Gang Wang,et al.  Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[11]  Xiaogang Wang,et al.  Unsupervised Salience Learning for Person Re-identification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Anton van den Hengel,et al.  Learning to rank in person re-identification with metric ensembles , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Yi Yang,et al.  Unsupervised Person Re-identification , 2018, ACM Trans. Multim. Comput. Commun. Appl..

[14]  Shaogang Gong,et al.  Towards unsupervised open-set person re-identification , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[15]  Alex ChiChung Kot,et al.  Domain Generalization with Adversarial Feature Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[16]  Yonghong Tian,et al.  CNN vs. SIFT for Image Retrieval: Alternative or Complementary? , 2016, ACM Multimedia.

[17]  Le Song,et al.  A Hilbert Space Embedding for Distributions , 2007, Discovery Science.

[18]  Bernhard Schölkopf,et al.  A Kernel Method for the Two-Sample-Problem , 2006, NIPS.

[19]  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.

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

[21]  Shaogang Gong,et al.  Learning a Discriminative Null Space for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Hai Tao,et al.  Evaluating Appearance Models for Recognition, Reacquisition, and Tracking , 2007 .

[23]  Wei-Shi Zheng,et al.  Cross-View Asymmetric Metric Learning for Unsupervised Person Re-Identification , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[24]  Horst Bischof,et al.  Large scale metric learning from equivalence constraints , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[26]  Lei Zhu,et al.  Unsupervised neighborhood component analysis for clustering , 2015, Neurocomputing.

[27]  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).

[28]  Ping Li,et al.  Cross-Domain Person Reidentification Using Domain Adaptation Ranking SVMs , 2015, IEEE Transactions on Image Processing.

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

[30]  Zaïd Harchaoui,et al.  A Fast, Consistent Kernel Two-Sample Test , 2009, NIPS.

[31]  Horst Bischof,et al.  Person Re-identification by Descriptive and Discriminative Classification , 2011, SCIA.

[32]  Bernhard Schölkopf,et al.  Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions , 2009, NIPS.

[33]  Vittorio Murino,et al.  Custom Pictorial Structures for Re-identification , 2011, BMVC.

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

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

[36]  Shaogang Gong,et al.  Dictionary Learning with Iterative Laplacian Regularisation for Unsupervised Person Re-identification , 2015, BMVC.

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

[38]  Xiaogang Wang,et al.  Person Re-Identification by Saliency Learning , 2014 .

[39]  Shaogang Gong,et al.  Domain transfer for person re-identification , 2013, ARTEMIS '13.

[40]  Jieping Ye,et al.  Adaptive Distance Metric Learning for Clustering , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[41]  Alessandro Perina,et al.  Person re-identification by symmetry-driven accumulation of local features , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[42]  Richard S. Zemel,et al.  Generative Moment Matching Networks , 2015, ICML.

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

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

[45]  Chang-Tsun Li,et al.  End-to-End Correspondence and Relationship Learning of Mid-Level Deep Features for Person Re-Identification , 2017, 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA).