{"type":"ScholarlyArticle","title":"Fast One-Stage Unsupervised Domain Adaptive Person Search","authors":[{"name":"Tianxiang Cui","slug":"tianxiang-cui","given":"Tianxiang","family":"Cui","affiliation":"The College of Information and Science Technology , Dalian Maritime University , China"},{"name":"Huibing Wang","slug":"huibing-wang","given":"Huibing","family":"Wang","affiliation":"The College of Information and Science Technology , Dalian Maritime University , China"},{"name":"Jinjia Peng","slug":"jinjia-peng","given":"Jinjia","family":"Peng","affiliation":"The College School of Cyber Security and Computer , Hebei University , China"},{"name":"Ruoxi Deng","slug":"ruoxi-deng","given":"Ruoxi","family":"Deng","affiliation":"The College of Computer Science and Artificial Intelligence , Wenzhou University , China"},{"name":"Xianping Fu","slug":"xianping-fu","given":"Xianping","family":"Fu","affiliation":"The College of Information and Science Technology , Dalian Maritime University , China"},{"name":"Yang Wang","slug":"yang-wang","given":"Yang","family":"Wang","affiliation":"Key Laboratory of Knowledge Engineering with Big Data , Hefei University of Technology , China"}],"abstract":"Unsupervised person search aims to localize a particular target person from a gallery set of scene images without annotations, which is extremely challenging due to the unexpected variations of the unlabeled domains. However, most existing methods dedicate to developing multi-stage models to adapt domain variations while using clustering for iterative model training, which inevitably increase model complexity. To address this issue, we propose a Fast One-stage Unsupervised person Search (FOUS) which complementaryly integrates domain adaption with label adaption within an end-to-end manner without iterative clustering. To minimize the domain discrepancy, FOUS introduced an Attention-based Domain Alignment Module (ADAM) which can not only align various domains for both detection and ReID tasks but also construct an attention mechanism to reduce the adverse impacts of low-quality candidates resulting from unsupervised detection. Moreover, to avoid the redundant iterative clustering mode, FOUS adopts a prototype-guided labeling method which minimizes redundant correlation computations for partial samples and assigns noisy coarse label groups efficiently. The coarse label groups will be continuously refined via label-flexible training network with an adaptive selection strategy. With the adapted domains and labels, FOUS can achieve the state-of-the-art (SOTA) performance on two benchmark datasets, CUHK-SYSU and PRW. The code is available at https://github.com/whbdmu/FOUS.","doi":"10.24963/ijcai.2024/79","url":"https://ijcai.org/proceedings/2024/79","pdf":"https://ijcai.org/proceedings/2024/0079.pdf","markdown":"https://ijcai.org/proceedings/2024/79.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/79","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":79,"pages":{"first":713,"last":721},"keywords":["Computer Vision: CV: Image and video retrieval","Computer Vision: CV: Recognition (object detection, categorization)"],"videos":[],"references":[{"position":1,"raw":"[Chang et al., 2018] Xiaojun Chang, Po-Yao Huang, Yi- Dong Shen, Xiaodan Liang, Yi Yang, and Alexander G Hauptmann. Rcaa: Relational context-aware agents for person search. In Proceedings of the European Confer- ence on Computer Vision (ECCV), pages 84-100, 2018.","title":"Rcaa: Relational context-aware agents for person search","authors":"Xiaojun Chang, Po-Yao Huang, Yi-Dong Shen, Xiaodan Liang, Yi Yang, Alexander G Hauptmann","year":"2018","venue":"Proceedings of the European Conference on Computer Vision (ECCV)","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chen et al., 2018a] Di Chen, Shanshan Zhang, Wanli Ouyang, Jian Yang, and Ying Tai. Person search via a mask-guided two-stream cnn model. In Proceedings of the european conference on computer vision (ECCV), pages 734-750, 2018.","title":"Person search via a mask-guided two-stream cnn model","authors":"Chen","year":"2018","venue":"Proceedings of the european conference on computer vision (ECCV)","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2018b] Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Domain adaptive faster r-cnn for object detection in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3339-3348, 2018.","title":"Domain adaptive faster r-cnn for object detection in the wild","authors":"Chen","year":"2018","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2020a] Di Chen, Shanshan Zhang, Wanli Ouyang, Jian Yang, and Bernt Schiele. Hierarchical online instance matching for person search. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 10518-10525, 2020.","title":"Hierarchical online instance matching for person search","authors":"Chen","year":"2020","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2020b] Di Chen, Shanshan Zhang, Jian Yang, and Bernt Schiele. Norm-aware embedding for efficient person search. In Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, pages 12615-12624, 2020.","title":"Norm-aware embedding for efficient person search","authors":"Chen","year":"2020","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Deng et al., 2009] Jia Deng, Wei Dong, Richard Socher, Li- Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255.","title":"Imagenet: A large-scale hierarchical image database","authors":"Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, Li Fei-Fei","year":"2009","venue":"IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Ding et al., 2020] Yuhang Ding, Hehe Fan, Mingliang Xu, and Yi Yang. Adaptive exploration for unsupervised per- son re-identification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 16(1):1-19, 2020.","title":"Adaptive exploration for unsupervised person re-identification","authors":"Yuhang Ding, Hehe Fan, Mingliang Xu, Yi Yang","year":"2020","venue":"ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Dong et al., 2020] Wenkai Dong, Zhaoxiang Zhang, Chun- feng Song, and Tieniu Tan. Instance guided proposal net- work for person search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2585-2594, 2020.","title":"Instance guided proposal network for person search","authors":"Dong","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Girshick et al., 2015] Ross Girshick, Forrest Iandola, Trevor Darrell, and Jitendra Malik. Deformable part models are convolutional neural networks. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 437-446, 2015.","title":"Deformable part models are convolutional neural networks","authors":"Ross Girshick, Forrest Iandola, Trevor Darrell, Jitendra Malik","year":"2015","venue":"Proceedings of the IEEE conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Han et al., 2019] Chuchu Han, Jiacheng Ye, Yunshan Zhong, Xin Tan, Chi Zhang, Changxin Gao, and Nong Sang. Re-id driven localization refinement for person search. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9814-9823, 2019.","title":"Re-id driven localization refinement for person search","authors":"Han","year":"2019","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Han et al., 2021] Chuchu Han, Kai Su, Dongdong Yu, Ze- huan Yuan, Changxin Gao, Nong Sang, Yi Yang, and Changhu Wang. Weakly supervised person search with re- gion siamese networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12006-12015, 2021.","title":"Weakly supervised person search with region siamese networks","authors":"Chuchu Han, Kai Su, Dongdong Yu, Zehuan Yuan, Changxin Gao, Nong Sang, Yi Yang, Changhu Wang","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Jia et al., 2023] Chengyou Jia, Minnan Luo, Caixia Yan, Linchao Zhu, Xiaojun Chang, and Qinghua Zheng. Col- laborative contrastive refining for weakly supervised per- son search. IEEE Transactions on Image Processing, 2023.","title":"Collaborative contrastive refining for weakly supervised person search","authors":"Jia","year":"2023","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Khodabandeh et al., 2019] Mehran Khodabandeh, Arash Vahdat, Mani Ranjbar, and William G Macready. A ro- bust learning approach to domain adaptive object detec- tion. In Proceedings of the IEEE/CVF International Con- ference on Computer Vision, pages 480-490, 2019.","title":"A robust learning approach to domain adaptive object detection","authors":"Mehran Khodabandeh, Arash Vahdat, Mani Ranjbar, William G Macready","year":"2019","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Lan et al., 2018] Xu Lan, Xiatian Zhu, and Shaogang Gong. Person search by multi-scale matching. In Proceedings of the European conference on computer vision (ECCV), pages 536-552, 2018.","title":"Person search by multi-scale matching","authors":"Xiatian Xu Lan, Shaogang Zhu, Gong","year":"2018","venue":"Proceedings of the European conference on computer vision (ECCV)","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Li and Miao, 2021] Zhengjia Li and Duoqian Miao. Se- quential end-to-end network for efficient person search. In Proceedings of the AAAI Conference on Artificial Intelli- gence, volume 35, pages 2011-2019, 2021.","title":"Zhengjia Li and Duoqian Miao. Sequential end-to-end network for efficient person search","authors":"Miao Li","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2019] Xiang Li, Xiaolin Hu, and Jian Yang. Spatial group-wise enhance: Improving semantic fea- ture learning in convolutional networks. arXiv preprint arXiv:1905.09646, 2019.","title":"Spatial group-wise enhance: Improving semantic feature learning in convolutional networks","authors":"Xiang Li, Xiaolin Hu, Jian Yang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Li et al., 2022] Junjie Li, Yichao Yan, Guanshuo Wang, Fufu Yu, Qiong Jia, and Shouhong Ding. Domain adap- tive person search. In European Conference on Computer Vision, pages 302-318. Springer, 2022.","title":"Domain adaptive person search","authors":"Junjie Li, Yichao Yan, Guanshuo Wang, Fufu Yu, Qiong Jia, Shouhong Ding","year":"2022","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Liu et al., 2017] Hao Liu, Jiashi Feng, Zequn Jie, Kar- lekar Jayashree, Bo Zhao, Meibin Qi, Jianguo Jiang, and Shuicheng Yan. Neural person search machines. In Pro- ceedings of the IEEE International Conference on Com- puter Vision, pages 493-501, 2017.","title":"Neural person search machines","authors":"Hao Liu, Jiashi Feng, Zequn Jie, Karlekar Jayashree, Bo Zhao, Meibin Qi, Jianguo Jiang, Shuicheng Yan","year":"2017","venue":"Proceedings of the IEEE International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Munjal et al., 2019] Bharti Munjal, Sikandar Amin, Fed- erico Tombari, and Fabio Galasso. Query-guided end- to-end person search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 811-820, 2019.","title":"Query-guided endto-end person search","authors":"Bharti Munjal, Sikandar Amin, Federico Tombari, Fabio Galasso","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Paszke et al., 2017] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary De- Vito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. 2017.","title":"Automatic differentiation in pytorch","authors":"Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary De-Vito, Zeming Lin, Alban Desmaison, Luca Antiga, Adam Lerer","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Peng et al., 2020] Jinjia Peng, Yang Wang, Huibing Wang, Zhao Zhang, Xianping Fu, and Meng Wang. Unsuper- vised vehicle re-identification with progressive adaptation. In Christian Bessiere, editor, Proceedings of the Twenty- Ninth International Joint Conference on Artificial Intelli- gence, IJCAI-20, pages 913-919. International Joint Con- ferences on Artificial Intelligence Organization, 7 2020. Main track.","title":"Unsupervised vehicle re-identification with progressive adaptation","authors":"Peng","year":"2020","venue":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20","doi":null,"url":null,"internal_url":"/proceedings/2020/127"},{"position":23,"raw":"[Ruan et al., 2021] Dongsheng Ruan, Daiyin Wang, Yuan Zheng, Nenggan Zheng, and Min Zheng. Gaussian con- text transformer. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 15129-15138, 2021.","title":"Gaussian context transformer","authors":"Dongsheng Ruan, Daiyin Wang, Yuan Zheng, Nenggan Zheng, Min Zheng","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Vibashan et al., 2023] VS Vibashan, Poojan Oza, and Vishal M Patel. Instance relation graph guided source- free domain adaptive object detection. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3520-3530. IEEE, 2023.","title":"Instance relation graph guided sourcefree domain adaptive object detection","authors":"Vibashan","year":"2023","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Wang et al., 2020a] Cheng Wang, Bingpeng Ma, Hong Chang, Shiguang Shan, and Xilin Chen. Tcts: A task- consistent two-stage framework for person search. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11952-11961, 2020.","title":"Tcts: A taskconsistent two-stage framework for person search","authors":"Wang","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Wang et al., 2020b] Qilong Wang, Banggu Wu, Pengfei Zhu, Peihua Li, Wangmeng Zuo, and Qinghua Hu. Eca- net: Efficient channel attention for deep convolutional neu- ral networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11534- 11542, 2020.","title":"Ecanet: Efficient channel attention for deep convolutional neural networks","authors":"Wang","year":"2020","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Wang et al., 2022] Yang Wang, Jinjia Peng, Huibing Wang, and Meng Wang. Progressive learning with multi-scale at- tention network for cross-domain vehicle re-identification. Science China Information Sciences, 65(6):160103, 2022.","title":"Progressive learning with multi-scale attention network for cross-domain vehicle re-identification","authors":"Yang Wang, Jinjia Peng, Huibing Wang, Meng Wang","year":"2022","venue":"Science China Information Sciences","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wang et al., 2023] Benzhi Wang, Yang Yang, Jinlin Wu, Guo-jun Qi, and Zhen Lei. Self-similarity driven scale- invariant learning for weakly supervised person search. arXiv preprint arXiv:2302.12986, 2023.","title":"Self-similarity driven scaleinvariant learning for weakly supervised person search","authors":"Wang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2024] Yang Wang, Biao Qian, Haipeng Liu, Yong Rui, and Meng Wang. Unpacking the gap box against data-free knowledge distillation. IEEE Transac- tions on Pattern Analysis and Machine Intelligence, 2024.","title":"Unpacking the gap box against data-free knowledge distillation","authors":"Yang Wang, Biao Qian, Haipeng Liu, Yong Rui, Meng Wang","year":"2024","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wang, 2021] Yang Wang. Survey on deep multi-modal data analytics: Collaboration, rivalry, and fusion. ACM Trans- actions on Multimedia Computing, Communications, and Applications (TOMM), 17(1s):1-25, 2021.","title":"Survey on deep multi-modal data analytics: Collaboration, rivalry, and fusion","authors":"Yang Wang","year":"2021","venue":"ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Woo et al., 2018] Sanghyun Woo, Jongchan Park, Joon- Young Lee, and In So Kweon. Cbam: Convolutional block attention module. In Proceedings of the European confer- ence on computer vision (ECCV), pages 3-19, 2018.","title":"Cbam: Convolutional block attention module","authors":"Sanghyun Woo, Jongchan Park, Joon-Young Lee, In So Kweon","year":"2018","venue":"Proceedings of the European conference on computer vision (ECCV)","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Xiao et al., 2017] Tong Xiao, Shuang Li, Bochao Wang, Liang Lin, and Xiaogang Wang. Joint detection and identi- fication feature learning for person search. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3415-3424, 2017.","title":"Joint detection and identification feature learning for person search","authors":"Tong Xiao, Shuang Li, Bochao Wang, Liang Lin, Xiaogang Wang","year":"2017","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Xiao et al., 2019] Jimin Xiao, Yanchun Xie, Tammam Tillo, Kaizhu Huang, Yunchao Wei, and Jiashi Feng. Ian: the individual aggregation network for person search. Pattern Recognition, 87:332-340, 2019.","title":"Ian: the individual aggregation network for person search","authors":"Jimin Xiao, Yanchun Xie, Tammam Tillo, Kaizhu Huang, Yunchao Wei, Jiashi Feng","year":"2019","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Xu et al., 2020] Chang-Dong Xu, Xing-Ran Zhao, Xin Jin, and Xiu-Shen Wei. Exploring categorical regularization for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 11724-11733, 2020.","title":"Exploring categorical regularization for domain adaptive object detection","authors":"Chang-Dong Xu, Xing-Ran Zhao, Xin Jin, Xiu-Shen Wei","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Yan et al., 2019] Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, and Xiaokang Yang. Learning context graph for person search. In Proceedings of the IEEE/CVF conference on computer vision and pat- tern recognition, pages 2158-2167, 2019.","title":"Learning context graph for person search","authors":"Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang","year":"2019","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Yan et al., 2021] Yichao Yan, Jinpeng Li, Jie Qin, Song Bai, Shengcai Liao, Li Liu, Fan Zhu, and Ling Shao. Anchor- free person search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7690-7699, 2021.","title":"Anchorfree person search","authors":"Yichao Yan, Jinpeng Li, Jie Qin, Song Bai, Shengcai Liao, Li Liu, Fan Zhu, Ling Shao","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Yan et al., 2022] Yichao Yan, Jinpeng Li, Shengcai Liao, Jie Qin, Bingbing Ni, Ke Lu, and Xiaokang Yang. Explor- ing visual context for weakly supervised person search. In Proceedings of the AAAI Conference on Artificial Intelli- gence, volume 36, pages 3027-3035, 2022.","title":"Exploring visual context for weakly supervised person search","authors":"Yichao Yan, Jinpeng Li, Shengcai Liao, Jie Qin, Bingbing Ni, Ke Lu, Xiaokang Yang","year":"2022","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[You et al., 2019] Kaichao You, Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan. Universal do- main adaptation. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition, pages 2720-2729, 2019.","title":"Universal domain adaptation","authors":"You","year":"2019","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zhang et al., 2023] Jingyi Zhang, Jiaxing Huang, Zhipeng Luo, Gongjie Zhang, Xiaoqin Zhang, and Shijian Lu. Da- detr: Domain adaptive detection transformer with infor- mation fusion. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 23787-23798, 2023.","title":"Dadetr: Domain adaptive detection transformer with information fusion","authors":"Jingyi Zhang, Jiaxing Huang, Zhipeng Luo, Gongjie Zhang, Xiaoqin Zhang, Shijian Lu","year":"2023","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Zheng et al., 2017] Liang Zheng, Hengheng Zhang, Shaoyan Sun, Manmohan Chandraker, Yi Yang, and Qi Tian. Person re-identification in the wild. In Pro- ceedings of the IEEE conference on computer vision and pattern recognition, pages 1367-1376, 2017.","title":"Person re-identification in the wild","authors":"Liang Zheng, Hengheng Zhang, Shaoyan Sun, Manmohan Chandraker, Yi Yang, Qi Tian","year":"2017","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Zhong et al., 2020] Yingji Zhong, Xiaoyu Wang, and Shil- iang Zhang. Robust partial matching for person search in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6827- 6835, 2020.","title":"Robust partial matching for person search in the wild","authors":"Zhong","year":"2020","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}