{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:32:04Z","timestamp":1750221124252,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":17,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,12,8]],"date-time":"2018-12-08T00:00:00Z","timestamp":1544227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,12,8]]},"DOI":"10.1145\/3297156.3297260","type":"proceedings-article","created":{"date-parts":[[2019,2,28]],"date-time":"2019-02-28T13:07:04Z","timestamp":1551359224000},"page":"286-290","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Raftnet"],"prefix":"10.1145","author":[{"given":"Wuhao","family":"Zhang","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Lizhuang","family":"Ma","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University &amp; East China Normal University, Shanghai, China"}]},{"given":"Yanhao","family":"Ge","sequence":"additional","affiliation":[{"name":"YouTu Lab, Tencent, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2018,12,8]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.177"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654966"},{"key":"e_1_3_2_1_3_1","volume-title":"A richly annotated dataset for pedestrian attribute recognition{J}. arXiv preprint arXiv:1603.07054","author":"Li D","year":"2016","unstructured":"Li D , Zhang Z , Chen X , A richly annotated dataset for pedestrian attribute recognition{J}. arXiv preprint arXiv:1603.07054 , 2016 . Li D, Zhang Z, Chen X, et al. A richly annotated dataset for pedestrian attribute recognition{J}. arXiv preprint arXiv:1603.07054, 2016."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2015.51"},{"key":"e_1_3_2_1_5_1","volume-title":"Weakly-supervised learning of mid-level features for pedestrian attribute recognition and localization{J}. arXiv preprint arXiv:1611.05603","author":"Yu K","year":"2016","unstructured":"Yu K , Leng B , Zhang Z , Weakly-supervised learning of mid-level features for pedestrian attribute recognition and localization{J}. arXiv preprint arXiv:1611.05603 , 2016 . Yu K, Leng B, Zhang Z, et al. Weakly-supervised learning of mid-level features for pedestrian attribute recognition and localization{J}. arXiv preprint arXiv:1611.05603, 2016."},{"key":"e_1_3_2_1_6_1","volume-title":"Hydraplus-net: Attentive deep features for pedestrian analysis{J}. arXiv preprint arXiv:1709.09930","author":"Liu X","year":"2017","unstructured":"Liu X , Zhao H , Tian M , Hydraplus-net: Attentive deep features for pedestrian analysis{J}. arXiv preprint arXiv:1709.09930 , 2017 . Liu X, Zhao H, Tian M, et al. Hydraplus-net: Attentive deep features for pedestrian analysis{J}. arXiv preprint arXiv:1709.09930, 2017."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Misra I Shrivastava A Gupta A etal Cross-stitch networks for multi-task learning{C}\/\/Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 3994--4003.  Misra I Shrivastava A Gupta A et al. Cross-stitch networks for multi-task learning{C}\/\/Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 3994--4003.","DOI":"10.1109\/CVPR.2016.433"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"e_1_3_2_1_9_1","volume-title":"Boult T E. Moon: A mixed objective optimization network for the recognition of facial attributes{C}\/\/European Conference on Computer Vision","author":"Rudd E M","year":"2016","unstructured":"Rudd E M , G\u00fcnther M , Boult T E. Moon: A mixed objective optimization network for the recognition of facial attributes{C}\/\/European Conference on Computer Vision . Springer , Cham , 2016 : 19--35. Rudd E M, G\u00fcnther M, Boult T E. Moon: A mixed objective optimization network for the recognition of facial attributes{C}\/\/European Conference on Computer Vision. Springer, Cham, 2016: 19--35."},{"key":"e_1_3_2_1_10_1","volume-title":"Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification{C}\/\/CVPR","author":"Lu Y","year":"2017","unstructured":"Lu Y , Kumar A , Zhai S , Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification{C}\/\/CVPR . 2017 , 1(2): 6. Lu Y, Kumar A, Zhai S, et al. Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification{C}\/\/CVPR. 2017, 1(2): 6."},{"key":"e_1_3_2_1_11_1","volume-title":"Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model{J}. arXiv preprint arXiv:1707.06089","author":"Sarfraz M S","year":"2017","unstructured":"Sarfraz M S , Schumann A , Wang Y , Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model{J}. arXiv preprint arXiv:1707.06089 , 2017 . Sarfraz M S, Schumann A, Wang Y, et al. Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model{J}. arXiv preprint arXiv:1707.06089, 2017."},{"key":"e_1_3_2_1_12_1","unstructured":"Li D Chen X Zhang Z etal POSE GUIDED DEEP MODEL FOR PEDESTRIAN ATTRIBUTE RECOGNITION IN SURVEILLANCE SCENARIOS{J}.  Li D Chen X Zhang Z et al. POSE GUIDED DEEP MODEL FOR PEDESTRIAN ATTRIBUTE RECOGNITION IN SURVEILLANCE SCENARIOS{J}."},{"key":"e_1_3_2_1_13_1","volume-title":"Improving person re-identification by attribute and identity learning{J}. arXiv preprint arXiv:1703.07220","author":"Lin Y","year":"2017","unstructured":"Lin Y , Zheng L , Zheng Z , Improving person re-identification by attribute and identity learning{J}. arXiv preprint arXiv:1703.07220 , 2017 . Lin Y, Zheng L, Zheng Z, et al. Improving person re-identification by attribute and identity learning{J}. arXiv preprint arXiv:1703.07220, 2017."},{"key":"e_1_3_2_1_14_1","volume-title":"R-cnns for pose estimation and action detection{J}. arXiv preprint arXiv:1406.5212","author":"Gkioxari G","year":"2014","unstructured":"Gkioxari G , Hariharan B , Girshick R , R-cnns for pose estimation and action detection{J}. arXiv preprint arXiv:1406.5212 , 2014 . Gkioxari G, Hariharan B, Girshick R, et al. R-cnns for pose estimation and action detection{J}. arXiv preprint arXiv:1406.5212, 2014."},{"key":"e_1_3_2_1_15_1","article-title":"A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition{J}","author":"Ranjan R","year":"2017","unstructured":"Ranjan R , Patel V M , Chellappa R. Hyperface : A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition{J} . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017 . Ranjan R, Patel V M, Chellappa R. Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition{J}. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"He K Zhang X Ren S etal Deep residual learning for image recognition{C}\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770--778.  He K Zhang X Ren S et al. Deep residual learning for image recognition{C}\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770--778.","DOI":"10.1109\/CVPR.2016.90"},{"volume-title":"2015 3rd IAPR Asian Conference on. IEEE","author":"Li D","key":"e_1_3_2_1_17_1","unstructured":"Li D , Chen X , Huang K. Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios{C}\/\/Pattern Recognition (ACPR) , 2015 3rd IAPR Asian Conference on. IEEE , 2015: 111--115. Li D, Chen X, Huang K. Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios{C}\/\/Pattern Recognition (ACPR), 2015 3rd IAPR Asian Conference on. IEEE, 2015: 111--115."}],"event":{"name":"CSAI '18: 2018 2nd International Conference on Computer Science and Artificial Intelligence","sponsor":["Shenzhen University Shenzhen University"],"location":"Shenzhen China","acronym":"CSAI '18"},"container-title":["Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3297156.3297260","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3297156.3297260","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:02:14Z","timestamp":1750208534000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3297156.3297260"}},"subtitle":["Extract Task-aware Features for Pedestrian Attribute Recognition"],"short-title":[],"issued":{"date-parts":[[2018,12,8]]},"references-count":17,"alternative-id":["10.1145\/3297156.3297260","10.1145\/3297156"],"URL":"https:\/\/doi.org\/10.1145\/3297156.3297260","relation":{},"subject":[],"published":{"date-parts":[[2018,12,8]]},"assertion":[{"value":"2018-12-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}