More Web Proxy on the site http://driver.im/

research-article

Clustering Matters: Sphere Feature for Fully Unsupervised Person Re-identification

Authors:

Abdulmotaleb El SaddikAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 18, Issue 4

Article No.: 97, Pages 1 - 18

https://doi.org/10.1145/3501404

Published: 15 March 2022 Publication History

Abstract

In person re-identification (Re-ID), the data annotation cost of supervised learning, is huge and it cannot adapt well to complex situations. Therefore, compared with supervised deep learning methods, unsupervised methods are more in line with actual needs. In unsupervised learning, a key to solving Re-ID is to find a standard that can effectively distinguish the difference (distance) between the features of images belonging to different pedestrian identities. However, there are some differences in the images captured by different cameras (such as brightness, angle, etc.). It is well known that the training of neural networks is mainly based on the distance between features, while in unsupervised learning, especially in unsupervised learning methods based on hierarchical clustering, the distance between features plays a more important role in the clustering phase. We improve the accuracy of a deep learning method based on hierarchical clustering under fully unsupervised conditions, starting from both feature and distance metrics. First, we propose to use spherical features, by normalizing the images in the feature space, to weaken the structural differences (length) between features, while saving the feature differences (direction) between different identities. Then, we use the sum of squared errors (SSE) as a regularization term to balance different cluster states. We evaluate our method on four large-scale Re-ID datasets, and experiments show that our method achieves better results than the state-of-the-art unsupervised methods.

References

[1]

Jean-Paul Ainam, Ke Qin, Guisong Liu, Guangchun Luo, and Brighter Agyemang. 2020. Enforcing affinity feature learning through self-attention for person re-identification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 16, 1 (2020), 1–22.

Digital Library

[2]

Zechen Bai, Zhigang Wang, Jian Wang, Di Hu, and Errui Ding. 2021. Unsupervised multi-source domain adaptation for person re-identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 12914–12923.

[3]

Yanbei Chen, Xiatian Zhu, and Shaogang Gong. 2018. Deep association learning for unsupervised video person re-identification. arXiv preprint arXiv:1808.07301 (2018).

[4]

De Cheng, Yihong Gong, Sanping Zhou, Jinjun Wang, and Nanning Zheng. 2016. Person re-identification by multi-channel parts-basedCNN with improved triplet loss function. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1335–1344.

[5]

Weijian Deng, Liang Zheng, Qixiang Ye, Guoliang Kang, Yi Yang, and Jianbin Jiao. 2018. Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 994–1003.

[6]

Guodong Ding, Salman Khan, Zhenmin Tang, Jian Zhang, and Fatih Porikli. 2019. Towards better validity: Dispersion based clustering for unsupervised person re-identification. arXiv preprint arXiv:1906.01308 (2019).

[7]

Shengyong Ding, Liang Lin, Guangrun Wang, and Hongyang Chao. 2015. Deep feature learning with relative distance comparison for person re-identification. Pattern Recognition 48, 10 (2015), 2993–3003.

Digital Library

[8]

Yuhang Ding, Hehe Fan, Mingliang Xu, and Yi Yang. 2020. Adaptive exploration for unsupervised person re-identification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 16, 1 (2020), 1–19.

Digital Library

[9]

Hehe Fan, Liang Zheng, Chenggang Yan, and Yi Yang. 2018. Unsupervised person re-identification: Clustering and fine-tuning. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 14, 4 (2018), 83.

[10]

Xing Fan, Wei Jiang, Hao Luo, and Mengjuan Fei. 2019. SphereReID: Deep hypersphere manifold embedding for person re-identification. Journal of Visual Communication and Image Representation 60 (2019), 51–58.

Digital Library

[11]

Aleksei Grigorev, Shaohui Liu, Zhihong Tian, Jianxin Xiong, Seungmin Rho, and Jiang Feng. 2020. Delving deeper in drone-based person Re-Id by employing deep decision forest and attributes fusion. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 16, 1s (2020), 1–15.

Digital Library

[12]

Wei Li, Rui Zhao, Tong Xiao, and Xiaogang Wang. 2014. DeepReID: Deep filter pairing neural network for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 152–159.

Digital Library

[13]

Yaoyu Li, Hantao Yao, Tianzhu Zhang, and Changsheng Xu. 2020. Part-based structured representation learning for person re-identification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 16, 4 (2020), 1–22.

Digital Library

[14]

Zhaoju Li, Zongwei Zhou, Nan Jiang, Zhenjun Han, Junliang Xing, and Jianbin Jiao. 2020. Spatial preserved graph convolution networks for person re-identification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 16, 1s (2020), 1–14.

Digital Library

[15]

Yutian Lin, Xuanyi Dong, Liang Zheng, Yan Yan, and Yi Yang. 2019. A bottom-up clustering approach to unsupervised person re-identification. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 8738–8745.

Digital Library

[16]

Yutian Lin, Lingxi Xie, Yu Wu, Chenggang Yan, and Qi Tian. 2020. Unsupervised person re-identification via softened similarity learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3390–3399.

[17]

Hao Liu, Jiashi Feng, Meibin Qi, Jianguo Jiang, and Shuicheng Yan. 2017. End-to-end comparative attention networks for person re-identification. IEEE Transactions on Image Processing 26, 7 (2017), 3492–3506.

Digital Library

[18]

Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, and Le Song. 2017. Sphereface: Deep hypersphere embedding for face recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 212–220.

[19]

Chuanchen Luo, Chunfeng Song, and Zhaoxiang Zhang. 2020. Generalizing person re-identification by camera-aware invariance learning and cross-domain mixup. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XV 16. Springer, 224–241.

Digital Library

[20]

Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, Nov (2008), 2579–2605.

[21]

Jiaxu Miao, Yu Wu, Ping Liu, Yuhang Ding, and Yi Yang. 2019. Pose-guided feature alignment for occluded person re-identification. In Proceedings of the IEEE International Conference on Computer Vision. 542–551.

[22]

Lei Qi, Lei Wang, Jing Huo, Yinghuan Shi, and Yang Gao. 2021. GreyReID: A novel two-stream deep framework with RGB-grey information for person re-identification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 17, 1 (2021), 1–22.

Digital Library

[23]

Xuelin Qian, Yanwei Fu, Tao Xiang, Wenxuan Wang, Jie Qiu, Yang Wu, Yu-Gang Jiang, and Xiangyang Xue. 2018. Pose-normalized image generation for person re-identification. In Proceedings of the European Conference on Computer Vision (ECCV). 650–667.

Digital Library

[24]

Ruijie Quan, Xuanyi Dong, Yu Wu, Linchao Zhu, and Yi Yang. 2019. Auto-ReID: Searching for a part-aware ConvNet for person re-identification. In Proceedings of the IEEE International Conference on Computer Vision. 3750–3759.

[25]

Ergys Ristani, Francesco Solera, Roger Zou, Rita Cucchiara, and Carlo Tomasi. 2016. Performance measures and a data set for multi-target, multi-camera tracking. In European Conference on Computer Vision Workshop on Benchmarking Multi-Target Tracking.

[26]

Ergys Ristani, Francesco Solera, Roger Zou, Rita Cucchiara, and Carlo Tomasi. 2016. Performance measures and a data set for multi-target, multi-camera tracking. In European Conference on Computer Vision. Springer, 17–35.

[27]

Weijian Ruan, Chao Liang, Yi Yu, Zheng Wang, Wu Liu, Jun Chen, and Jiayi Ma. 2020. Correlation discrepancy insight network for video re-identification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 16, 4 (2020), 1–21.

Digital Library

[28]

Hailin Shi, Yang Yang, Xiangyu Zhu, Shengcai Liao, Zhen Lei, Weishi Zheng, and Stan Z. Li. 2016. Embedding deep metric for person re-identification: A study against large variations. In European Conference on Computer Vision. Springer, 732–748.

[29]

Jingkuan Song, Yuyu Guo, Lianli Gao, Xuelong Li, Alan Hanjalic, and Heng Tao Shen. 2018. From deterministic to generative: Multimodal stochastic RNNs for video captioning. IEEE Transactions on Neural Networks and Learning Systems 30, 10 (2018), 3047–3058.

[30]

Jingkuan Song, Hanwang Zhang, Xiangpeng Li, Lianli Gao, Meng Wang, and Richang Hong. 2018. Self-supervised video hashing with hierarchical binary auto-encoder. IEEE Transactions on Image Processing 27, 7 (2018), 3210–3221.

[31]

Chi Su, Jianing Li, Shiliang Zhang, Junliang Xing, Wen Gao, and Qi Tian. 2017. Pose-driven deep convolutional model for person re-identification. In Proceedings of the IEEE International Conference on Computer Vision. 3960–3969.

[32]

Xiaoxiao Sun and Liang Zheng. 2019. Dissecting person re-identification from the viewpoint of viewpoint. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 608–617.

[33]

Yifan Sun, Liang Zheng, Yi Yang, Qi Tian, and Shengjin Wang. 2018. Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In Proceedings of the European Conference on Computer Vision (ECCV). 480–496.

Digital Library

[34]

Maoqing Tian, Shuai Yi, Hongsheng Li, Shihua Li, Xuesen Zhang, Jianping Shi, Junjie Yan, and Xiaogang Wang. 2018. Eliminating background-bias for robust person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5794–5803.

[35]

Rahul Rama Varior, Mrinal Haloi, and Gang Wang. 2016. Gated siamese convolutional neural network architecture for human re-identification. In European Conference on Computer Vision. Springer, 791–808.

[36]

Rahul Rama Varior, Bing Shuai, Jiwen Lu, Dong Xu, and Gang Wang. 2016. A siamese long short-term memory architecture for human re-identification. In European Conference on Computer Vision. Springer, 135–153.

[37]

Dongkai Wang and Shiliang Zhang. 2020. Unsupervised person re-identification via multi-label classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10981–10990.

Digital Library

[38]

Guanshuo Wang, Yufeng Yuan, Xiong Chen, Jiwei Li, and Xi Zhou. 2018. Learning discriminative features with multiple granularities for person re-identification. In 2018 ACM Multimedia Conference on Multimedia Conference. ACM, 274–282.

Digital Library

[39]

Jin Wang, Zheng Wang, Chao Liang, Changxin Gao, and Nong Sang. 2018. Equidistance constrained metric learning for person re-identification. Pattern Recognition 74 (2018), 38–51.

Digital Library

[40]

Jingya Wang, Xiatian Zhu, Shaogang Gong, and Wei Li. 2018. Transferable joint attribute-identity deep learning for unsupervised person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2275–2284.

[41]

Longhui Wei, Shiliang Zhang, Wen Gao, and Qi Tian. 2018. Person transfer GAN to bridge domain gap for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 79–88.

[42]

Yu Wu, Yutian Lin, Xuanyi Dong, Yan Yan, Wei Bian, and Yi Yang. 2019. Progressive learning for person re-identification with one example. IEEE Transactions on Image Processing 28, 6 (2019), 2872–2881.

[43]

Yu Wu, Yutian Lin, Xuanyi Dong, Yan Yan, Wanli Ouyang, and Yi Yang. 2018. Exploit the unknown gradually: One-shot video-based person re-identification by stepwise learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5177–5186.

[44]

Tong Xiao, Shuang Li, Bochao Wang, Liang Lin, and Xiaogang Wang. 2017. Joint detection and identification feature learning for person search. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3415–3424.

[45]

Junyuan Xie, Ross Girshick, and Ali Farhadi. 2016. Unsupervised deep embedding for clustering analysis. In International Conference on Machine Learning. 478–487.

Digital Library

[46]

Eric P. Xing, Michael I. Jordan, Stuart J. Russell, and Andrew Y. Ng. 2003. Distance metric learning with application to clustering with side-information. In Advances in Neural Information Processing Systems. 521–528.

Digital Library

[47]

Qize Yang, Hong-Xing Yu, Ancong Wu, and Wei-Shi Zheng. 2019. Patch-based discriminative feature learning for unsupervised person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3633–3642.

[48]

Mang Ye, Xiangyuan Lan, and Pong C. Yuen. 2018. Robust anchor embedding for unsupervised video person re-identification in the wild. In Proceedings of the European Conference on Computer Vision (ECCV). 170–186.

[49]

Mang Ye, Jiawei Li, Andy J. Ma, Liang Zheng, and Pong C. Yuen. 2019. Dynamic graph co-matching for unsupervised video-based person re-identification. IEEE Transactions on Image Processing 28, 6 (2019), 2976–2990.

[50]

Dong Yi, Zhen Lei, Shengcai Liao, and Stan Z. Li. 2014. Deep metric learning for person re-identification. In 2014 22nd International Conference on Pattern Recognition. IEEE, 34–39.

Digital Library

[51]

Hong-Xing Yu, Ancong Wu, and Wei-Shi Zheng. 2017. Cross-view asymmetric metric learning for unsupervised person re-identification. In Proceedings of the IEEE International Conference on Computer Vision. 994–1002.

[52]

Hong-Xing Yu, Wei-Shi Zheng, Ancong Wu, Xiaowei Guo, Shaogang Gong, and Jian-Huang Lai. 2019. Unsupervised person re-identification by soft multilabel learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2148–2157.

[53]

Tianyuan Yu, Da Li, Yongxin Yang, Timothy M. Hospedales, and Tao Xiang. 2019. Robust person re-identification by modelling feature uncertainty. In Proceedings of the IEEE International Conference on Computer Vision. 552–561.

[54]

Kaiwei Zeng, Munan Ning, Yaohua Wang, and Yang Guo. 2020. Hierarchical clustering with hard-batch triplet loss for person re-identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 13657–13665.

[55]

Anguo Zhang, Yueming Gao, Yuzhen Niu, Wenxi Liu, and Yongcheng Zhou. 2021. Coarse-to-fine person re-identification with auxiliary-domain classification and second-order information bottleneck. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 598–607.

[56]

Xuan Zhang, Hao Luo, Xing Fan, Weilai Xiang, Yixiao Sun, Qiqi Xiao, Wei Jiang, Chi Zhang, and Jian Sun. 2017. AlignedReID: Surpassing human-level performance in person re-identification. arXiv preprint arXiv:1711.08184.

[57]

Liang Zheng, Zhi Bie, Yifan Sun, Jingdong Wang, Chi Su, Shengjin Wang, and Qi Tian. 2016. MARS: A video benchmark for large-scale person re-identification. In European Conference on Computer Vision. Springer, 868–884.

[58]

Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. 2015. Scalable person re-identification: A benchmark. In Proceedings of the IEEE International Conference on Computer Vision. 1116–1124.

Digital Library

[59]

Liang Zheng, Hengheng Zhang, Shaoyan Sun, Manmohan Chandraker, Yi Yang, and Qi Tian. 2017. Person re-identification in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1367–1376.

[60]

Yi Zheng, Yong Zhou, Jiaqi Zhao, Meng Jian, Rui Yao, Bing Liu, and Xuning Liu. 2019. A siamese pedestrian alignment network for person re-identification. In Chinese Conference on Pattern Recognition and Computer Vision (PRCV). Springer, 409–420.

Digital Library

[61]

Zhedong Zheng, Xiaodong Yang, Zhiding Yu, Liang Zheng, Yi Yang, and Jan Kautz. 2019. Joint discriminative and generative learning for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2138–2147.

[62]

Zhedong Zheng, Liang Zheng, and Yi Yang. 2017. A discriminatively learned CNN embedding for person reidentification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 14, 1 (2017), 1–20.

Digital Library

[63]

Zhedong Zheng, Liang Zheng, and Yi Yang. 2017. Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In Proceedings of the IEEE International Conference on Computer Vision. 3754–3762.

[64]

Zhedong Zheng, Liang Zheng, and Yi Yang. 2018. Pedestrian alignment network for large-scale person re-identification. IEEE Transactions on Circuits and Systems for Video Technology.

[65]

Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang. 2020. Random erasing data augmentation. In AAAI. 13001–13008.

[66]

Zhun Zhong, Liang Zheng, Zhiming Luo, Shaozi Li, and Yi Yang. 2019. Invariance matters: Exemplar memory for domain adaptive person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 598–607.

[67]

Zhun Zhong, Liang Zheng, Zhedong Zheng, Shaozi Li, and Yi Yang. 2018. CamStyle: A novel data augmentation method for person re-identification. IEEE Transactions on Image Processing 28, 3 (2018), 1176–1190.

Digital Library

Cited By

Xiong MHu KLyu ZFang FWang ZHu RMuhammad K(2024)Inter-camera Identity Discrimination for Unsupervised Person Re-identificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365285820:8(1-18)Online publication date: 13-Jun-2024
https://dl.acm.org/doi/10.1145/3652858
Li ZShi YLing HChen JLiu BWang RZhao C(2024)Viewpoint Disentangling and Generation for Unsupervised Object Re-IDACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363295920:5(1-23)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1145/3632959
Liu CYang S(2024)A two-stage clustering ensemble algorithm applicable to risk assessment of railway signaling faultsExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123500249:PAOnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123500
Show More Cited By

Index Terms

Recommendations

Unsupervised Person Re-identification: Clustering and Fine-tuning
Special Section on Deep Learning for Intelligent Multimedia Analytics

The superiority of deeply learned pedestrian representations has been reported in very recent literature of person re-identification (re-ID). In this article, we consider the more pragmatic issue of learning a deep feature with no or only a few labels. ...
Hybrid feature constraint with clustering for unsupervised person re-identification
Abstract
Unsupervised person re-identification (Re-ID) has better scalability and usability in real-world deployments due to the lack of annotations, which is more challenging than supervised methods. State-of-the-art approaches mainly employ clustering ...
Adaptive Scheme of Clustering-Based Unsupervised Learning for Person Re-identification
Intelligent Information and Database Systems
Abstract
Clustering and cluster-level contrastive learning are widely used functions in unsupervised re-identification methods, with a focus on extracting robust and distinctive features from data without annotations. However, existing approaches often ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 18, Issue 4

November 2022

497 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3514185

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 March 2022

Accepted: 01 November 2021

Revised: 01 November 2021

Received: 01 March 2021

Published in TOMM Volume 18, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Funding Sources

National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province
Six Talent Peaks Project in Jiangsu Province

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
716
Total Downloads

Downloads (Last 12 months)64
Downloads (Last 6 weeks)10

Reflects downloads up to 13 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Xiong MHu KLyu ZFang FWang ZHu RMuhammad K(2024)Inter-camera Identity Discrimination for Unsupervised Person Re-identificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365285820:8(1-18)Online publication date: 13-Jun-2024
https://dl.acm.org/doi/10.1145/3652858
Li ZShi YLing HChen JLiu BWang RZhao C(2024)Viewpoint Disentangling and Generation for Unsupervised Object Re-IDACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363295920:5(1-23)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1145/3632959
Liu CYang S(2024)A two-stage clustering ensemble algorithm applicable to risk assessment of railway signaling faultsExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123500249:PAOnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123500
Zheng YZhao JZhou YLiu FYao RZhu HEl Saddik A(2024)Multi-level self attention for unsupervised learning person re-identificationMultimedia Tools and Applications10.1007/s11042-024-19007-z83:26(68855-68874)Online publication date: 24-Apr-2024
https://doi.org/10.1007/s11042-024-19007-z
Chen XZheng XLu X(2023)Identity Feature Disentanglement for Visible-Infrared Person Re-IdentificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/359518319:6(1-20)Online publication date: 12-Jul-2023
https://dl.acm.org/doi/10.1145/3595183
Weng ZWu ZLi HChen JJiang Y(2023)HCMS: Hierarchical and Conditional Modality Selection for Efficient Video RecognitionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/357277620:2(1-18)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1145/3572776
Shi HWang HMa RHua YSong TGao HGuan H(2023)Robust Searching-Based Gradient Collaborative Management in Intelligent Transportation SystemACM Transactions on Multimedia Computing, Communications, and Applications10.1145/354993920:2(1-23)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1145/3549939
Guan WSong XWang KWen HNi HWang YChang X(2023)Egocentric Early Action Prediction via Multimodal Transformer-Based Dual Action PredictionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.324827133:9(4472-4483)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3248271
Zhao YShu QShi XZhan J(2023)Unsupervised person re-identification by dynamic hybrid contrastive learningImage and Vision Computing10.1016/j.imavis.2023.104786137:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.imavis.2023.104786
Zhao YShu Q(2023)Unsupervised person re-identification by patch-based nearest neighbor miningDigital Signal Processing10.1016/j.dsp.2022.103885133:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.dsp.2022.103885
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents