Papers.md

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Today I will summarize the recent Attribute Re-ID Methods:

Improving person re-identification by attribute and identity learning

1. ICCV 2017
2. Lin, Yutian and Zheng, Liang and Zheng, Zhedong and Wu, Yu and Yang, Yi
3. 190209(1)Improving Person Re-identification by Attribute and Identity Learning.pdf

• Person re-identification (re-ID) and attribute recognition share a common target at the pedestrian description. Their difference consists in the granularity. Attribute recognition focuses on local aspects of a person while person re-ID usually extracts global representations.

  

• Considering their similarity and difference, this paper proposes a very simple convolutional neural network (CNN) that learns a re-ID embedding and predicts the pedestrian attributes simultaneously.

  

• This multi-task method integrates an ID classification loss and a number of attribute classification losses, and back-propagates the weighted sum of the individual losses. Albeit simple, we demonstrate on two pedestrian benchmarks that by learning a more discriminative representation, our method significantly improves the re-ID baseline and is scalable on large galleries. We report competitive reID performance compared with the state-of-the-art methods on the two datasets.

@article{lin2017improving, title={Improving person re-identification by attribute and identity learning}, author={Lin, Yutian and Zheng, Liang and Zheng, Zhedong and Wu, Yu and Yang, Yi}, journal={arXiv preprint arXiv:1703.07220}, year={2017} }

Multi-Task Learning with Low Rank Attribute Embedding for Multi-Camera Person Re-Identification

1. TPAMI 2018
2. Su, Chi and Yang, Fan and Zhang, Shiliang and Tian, Qi and Davis, Larry Steven and Gao, Wen
3. 190209(2)Multi-Task Learning with Low Rank Attribute Embedding for Multi-Camera Person Re-Identification.pdf

• We propose Multi-Task Learning with Low Rank Attribute Embedding (MTL-LORAE) to address the problem of person reidentification on multi-cameras. Re-identifications on different cameras are considered as related tasks, which allows the shared information among different tasks to be explored to improve the re-identification accuracy.

  

• The MTL-LORAE framework integrates lowlevel features with mid-level attributes as the descriptions for persons. To improve the accuracy of such description, we introduce the low-rank attribute embedding, which maps original binary attributes into a continuous space utilizing the correlative relationship between each pair of attributes.

  

• In this way, inaccurate attributes are rectified and missing attributes are recovered. The resulting objective function is constructed with an attribute embedding error and a quadratic loss concerning class labels. It is solved by an alternating optimization strategy. The proposed MTL-LORAE is tested on four datasets and is validated to outperform the existing methods with significant margins.

@article{su2018multi, title={Multi-task learning with low rank attribute embedding for multi-camera person re-identification}, author={Su, Chi and Yang, Fan and Zhang, Shiliang and Tian, Qi and Davis, Larry Steven and Gao, Wen}, journal={IEEE transactions on pattern analysis and machine intelligence}, volume={40}, number={5}, pages={1167--1181}, year={2018}, publisher={IEEE} }

Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification

1. CVPR 2018
2. Wang, Jingya and Zhu, Xiatian and Gong, Shaogang and Li, Wei
3. 190209(3)Wang_Transferable_Joint_Attribute-Identity_CVPR_2018_paper.pdf

• Most existing person re-identification (re-id) methods require supervised model learning from a separate large set of pairwise labelled training data for every single camera pair.

  

• This significantly limits their scalability and usability in real-world large scale deployments with the need for performing re-id across many camera views. To address this scalability problem, we develop a novel deep learning method for transferring the labelled information of an existing dataset to a new unseen (unlabelled) target domain for person re-id without any supervised learning in the target domain.

  

• Specifically, we introduce an Transferable Joint Attribute-Identity Deep Learning (TJ-AIDL) for simultaneously learning an attribute-semantic and identitydiscriminative feature representation space transferrable to any new (unseen) target domain for re-id tasks without the need for collecting new labelled training data from the target domain (i.e. unsupervised learning in the target domain). Extensive comparative evaluations validate the superiority of this new TJ-AIDL model for unsupervised person re-id over a wide range of state-of- the-art methods on four challenging benchmarks including VIPeR, PRID, Market-1501, and DukeMTMC-ReID.

@InProceedings{Wang_2018_CVPR, author = {Wang, Jingya and Zhu, Xiatian and Gong, Shaogang and Li, Wei}, title = {Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2018} }

Multi-type attributes driven multi-camera person re-identification

1. Pattern Recognition 2018
2. Su, Chi and Zhang, Shiliang and Xing, Junliang and Gao, Wen and Tian, Qi
3. 190209(4)Multi-typeattributesdrivenmulti-camerapersonre-identification.pdf

• One of the major challenges in person Re-Identification (ReID) is the inconsistent visual appearance of a person. Current works on visual feature and distance metric learning have achieved significant achieve- ments, but still suffer from the limited robustness to pose variations, viewpoint changes, etc ., and the high computational complexity.

  

• This makes person ReID among multiple cameras still challenging. This work is motivated to learn mid-level human attributes which are robust to visual appearance variations and could be used as efficient features for person matching.

  

• We propose a weakly supervised multi-type attribute learning framework which considers the contextual cues among attributes and progressively boosts the accuracy of attributes only using a limited number of labeled data. Specifically, this frame- work involves a three-stage training. A deep Convolutional Neural Network (dCNN) is first trained on an independent dataset labeled with attributes. Then it is fine-tuned on another dataset only labeled with person IDs using our defined triplet loss.

  

• Finally, the updated dCNN predicts attribute labels for the target dataset, which is combined with the independent dataset for the final round of fine-tuning. The predicted attributes, namely deep attributes exhibit promising generalization ability across different datasets. By di- rectly using the deep attributes with simple Cosine distance, we have obtained competitive accuracy on four person ReID datasets. Experiments also show that a simple distance metric learning modular further boosts our method, making it outperform many recent works.

@article{su2018multi, title={Multi-type attributes driven multi-camera person re-identification}, author={Su, Chi and Zhang, Shiliang and Xing, Junliang and Gao, Wen and Tian, Qi}, journal={Pattern Recognition}, volume={75}, pages={77--89}, year={2018}, publisher={Elsevier} }

@inproceedings{chang2018multi, title={Multi-level factorisation net for person re-identification}, author={Chang, Xiaobin and Hospedales, Timothy M and Xiang, Tao}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages={2109--2118}, year={2018} }

Multi-Level Factorisation Net for Person Re-Identification

1. CVPR 2018
2. Chang, Xiaobin and Hospedales, Timothy M. and Xiang, Tao
3. 190209(5)Chang_Multi-Level_Factorisation_Net_CVPR_2018_paper.pdf

• Key to effective person re-identification (Re-ID) is modelling discriminative and view-invariant factors of person appearance at both high and low semantic levels. Recently developed deep Re-ID models either learn a holistic single semantic level feature representation and/or require laborious human annotation of these factors as attributes.

  

• We propose Multi-Level Factorisation Net (MLFN), a novel network architecture that factorises the visual appearance of a person into latent discriminative factors at multiple semantic levels without manual annotation. MLFN is composed of multiple stacked blocks.

  

• Each block contains multiple factor modules to model latent factors at a specific level, and factor selection modules that dynamically select the factor modules to interpret the content of each input image.

• The outputs of the factor selection modules also provide a compact latent factor descriptor that is complementary to the conventional deeply learned features. MLFN achieves state-of-the-art results on three Re-ID datasets, as well as compelling results on the general object categorisation CIFAR-100 dataset.

@InProceedings{Chang_2018_CVPR, author = {Chang, Xiaobin and Hospedales, Timothy M. and Xiang, Tao}, title = {Multi-Level Factorisation Net for Person Re-Identification}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2018} }

Attributes-aided Part Detection and Refinement for Person Re-identification

1. arXiv 2019 02
2. Li, Shuzhao and Yu, Huimin and Huang, Wei and Zhang, Jing
3. 190209(6)Attributes-aided Part Detection and Refinement for Person Re-identification.pdf

• Person attributes are often exploited as mid-level human semantic information to help promote the performance of person re-identification task.

  

• In this paper, unlike most existing methods simply taking attribute learning as a classification problem, we perform it in a different way with the motivation that attributes are related to specific local regions, which refers to the perceptual ability of attributes. We utilize the process of attribute detection to generate corresponding attribute-part detectors, whose invariance to many influences like poses and camera views can be guaranteed.

  

• With detected local part regions, our model extracts local features to handle the body part misalignment problem, which is another major challenge for person re-identification. The local descriptors are further refined by fused attribute information to eliminate interferences caused by detection deviation. Extensive experiments on two popular benchmarks with attribute annotations demonstrate the effectiveness of our model and competitive performance compared with state-of-the-art algorithms

@article{li2019attributes, title={Attributes-aided Part Detection and Refinement for Person Re-identification}, author={Li, Shuzhao and Yu, Huimin and Huang, Wei and Zhang, Jing}, journal={arXiv preprint arXiv:1902.10528}, year={2019} }

Attention driven person re-identification

1. Pattern Recognition 2018
2. Fan Yang and Ke Yan and Shijian Lu and Huizhu Jia and Xiaodong Xie and Wen Gao
3. 190209(7)Attention driven person re-identification.pdf

• Person re-identification (ReID) is a challenging task due to arbitrary human pose variations, background clutters, etc.

  

• It has been studied extensively in recent years, but the multifarious local and global fea- tures are still not fully exploited by either ignoring the interplay between whole-body images and body- part images or missing in-depth examination of specific body-part images.

  

• In this paper, we propose a novel attention-driven multi-branch network that learns robust and discriminative human representation from global whole-body images and local body-part images simultaneously. Within each branch, an intra- attention network is designed to search for informative and discriminative regions within the whole-body or body-part images, where attention is elegantly decomposed into spatial-wise attention and channel- wise attention for effective and efficient learning.

  

• In addition, a novel inter-attention module is designed which fuses the output of intra-attention networks adaptively for optimal person ReID. The proposed technique has been evaluated over three widely used datasets CUHK03, Market-1501 and DukeMTMC- ReID, and experiments demonstrate its superior robustness and effectiveness as compared with the state of the arts.

@article{YANG2019143, title = "Attention driven person re-identification", journal = "Pattern Recognition", volume = "86", pages = "143 - 155", year = "2019", issn = "0031-3203", doi = "https://doi.org/10.1016/j.patcog.2018.08.015", url = "http://www.sciencedirect.com/science/article/pii/S0031320318303133", author = "Fan Yang and Ke Yan and Shijian Lu and Huizhu Jia and Xiaodong Xie and Wen Gao", keywords = "Person re-identification, Visual attention, Pose estimation, Deep neural networks", abstract = "Person re-identification (ReID) is a challenging task due to arbitrary human pose variations, background clutters, etc. It has been studied extensively in recent years, but the multifarious local and global features are still not fully exploited by either ignoring the interplay between whole-body images and body-part images or missing in-depth examination of specific body-part images. In this paper, we propose a novel attention-driven multi-branch network that learns robust and discriminative human representation from global whole-body images and local body-part images simultaneously. Within each branch, an intra-attention network is designed to search for informative and discriminative regions within the whole-body or body-part images, where attention is elegantly decomposed into spatial-wise attention and channel-wise attention for effective and efficient learning. In addition, a novel inter-attention module is designed which fuses the output of intra-attention networks adaptively for optimal person ReID. The proposed technique has been evaluated over three widely used datasets CUHK03, Market-1501 and DukeMTMC-ReID, and experiments demonstrate its superior robustness and effectiveness as compared with the state of the arts." }

Enhancing Person Retrieval with Joint Person Detection, Attribute Learning, and Identification

1. PCM 2018
2. Wu, Jianwen and Zhao, Ye and Liu, Xueliang
3. 190209(8)PCM_Wu2018_Chapter_EnhancingPersonRetrievalWithJo.pdf

• Person re-identification receives increasing attention in recent years.

  

• However, most works assume the persons have been well cropped from the whole scene images, and only focus on learning features and metrics. This paper considers the person re-identification problem in a real-world scenario, which should consider detection and identification simultaneously.

  

• This paper proposes a multi-task learning framework for person retrieval in the wild. Person attribute learning is exploited in our framework to enhance person retrieval.

  

• Our work consists of two main contributions: (1) we present a 11 image-level attribute annotations for each image in the large-scale PRW [27] dataset, and (2) we develop an end-to-end person retrieval framework which jointly learns person detec- tor, attribute detectors, and visual embeddings in a multi-task learn- ing manner. We evaluate the effectiveness of the proposed approach on two tasks, i.e. person attribute recognition and person re-identification. Experimental results have demonstrated the effectiveness of the proposed approach.

@inproceedings{wu2018enhancing, title={Enhancing Person Retrieval with Joint Person Detection, Attribute Learning, and Identification}, author={Wu, Jianwen and Zhao, Ye and Liu, Xueliang}, booktitle={Pacific Rim Conference on Multimedia}, pages={113--124}, year={2018}, organization={Springer} }

Deep attributes driven multi-camera person re-identification

1. ECCV 2016
2. Su, Chi and Zhang, Shiliang and Xing, Junliang and Gao, Wen and Tian, Qi
3. 190209(9)ECCV_2016_Deep Attributes Driven Multi-Camera Person.pdf

• The visual appearance of a person is easily affected by many factors like pose variations, viewpoint changes and camera parameter differences. This makes person Re-Identification (ReID) among multiple cameras a very challenging task. This work is motivated to learn mid-level human attributes which are robust to such visual appearance variations.

  

• And we propose a semi-supervised attribute learning framework which progressively boosts the accuracy of attributes only using a limited number of labeled data. Specifically, this framework involves a three-stage training. A deep Convolutional Neural Network (dCNN) is first trained on an independent dataset labeled with attributes. Then it is fine-tuned on another dataset only labeled with person IDs using our defined triplet loss. Finally, the updated dCNN predicts attribute labels for the target dataset, which is combined with the independent dataset for the final round of fine-tuning. The predicted attributes, namely deep attributes exhibit superior generalization ability across different datasets. By directly using the deep attributes with simple Cosine distance, we have obtained surprisingly good accuracy on four person ReID datasets. Experiments also show that a simple distance metric learning modular further boosts our method, making it significantly outperform many recent works.

@inproceedings{su2016deep, title={Deep attributes driven multi-camera person re-identification}, author={Su, Chi and Zhang, Shiliang and Xing, Junliang and Gao, Wen and Tian, Qi}, booktitle={European conference on computer vision}, pages={475--491}, year={2016}, organization={Springer} }

Person re-identification using cnn features learned from combination of attributes

1. ICPR 2016
2. Matsukawa, Tetsu and Suzuki, Einoshi
3. 190209(10)icpr2016_Person Re-Identification Using CNN Features.pdf

• Abstract—This paper presents fine-tuned CNN features for person re-identification. Recently, features extracted from top layers of pre-trained Convolutional Neural Network (CNN) on a large annotated dataset, e.g., ImageNet, have been proven to be strong off-the-shelf descriptors for various recognition tasks. However, large disparity among the pre-trained task, i.e., ImageNet classification, and the target task, i.e., person image matching, limits performances of the CNN features for person re-identification.

  

• In this paper, we improve the CNN features by conducting a fine-tuning on a pedestrian attribute dataset. In addition to the classification loss for multiple pedestrian attribute labels, we propose new labels by combining different attribute labels and use them for an additional classification loss function. The combination attribute loss forces CNN to distinguish more person specific information, yielding more discriminative features. After extracting features from the learned CNN, we apply conventional metric learning on a target re-identification dataset for further increasing discriminative power. Experimental results on four challenging person re-identification datasets (VIPeR, CUHK, PRID450S and GRID) demonstrate the effectiveness of the proposed features.

@inproceedings{matsukawa2016person, title={Person re-identification using cnn features learned from combination of attributes}, author={Matsukawa, Tetsu and Suzuki, Einoshin}, booktitle={2016 23rd International Conference on Pattern Recognition (ICPR)}, pages={2428--2433}, year={2016}, organization={IEEE} }