{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:17:37Z","timestamp":1761581857637},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Recently, person re-identification (re-ID) has attracted increasing research attention, which has broad application prospects in video surveillance and beyond. To this end, most existing methods highly relied on well-aligned pedestrian images and hand-engineered part-based model on the coarsest feature map. In this paper, to lighten the restriction of such fixed and coarse input alignment, an end-to-end part power set model with multi-scale features is proposed, which captures the discriminative parts of pedestrians from global to local, and from coarse to fine, enabling part-based scale-free person re-ID. In particular, we first factorize the visual appearance by enumerating $k$-combinations for all $k$ of $n$ body parts to exploit rich global and partial information to learn discriminative feature maps. Then, a combination ranking module is introduced to guide the model training with all combinations of body parts, which alternates between ranking combinations and estimating an appearance model. To enable scale-free input, we further exploit the pyramid architecture of deep networks to construct multi-scale feature maps with a feasible amount of extra cost in term of memory and time. Extensive experiments on the mainstream evaluation datasets, including Market-1501, DukeMTMC-reID and CUHK03, validate that our method achieves the state-of-the-art performance.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/471","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"3397-3403","source":"Crossref","is-referenced-by-count":8,"title":["A Part Power Set Model for Scale-Free Person Retrieval"],"prefix":"10.24963","author":[{"given":"Yunhang","family":"Shen","sequence":"first","affiliation":[{"name":"Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, 361005, China"}]},{"given":"Rongrong","family":"Ji","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, 361005, China"},{"name":"Peng Cheng Laborotory, China"}]},{"given":"Xiaopeng","family":"Hong","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong University, China"},{"name":"University of Oulu, Finland"}]},{"given":"Feng","family":"Zheng","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology"}]},{"given":"Xiaowei","family":"Guo","sequence":"additional","affiliation":[{"name":"Tencent Youtu Lab, Tencent Technology (Shanghai) Co., Ltd."}]},{"given":"Yongjian","family":"Wu","sequence":"additional","affiliation":[{"name":"Tencent Youtu Lab, Tencent Technology (Shanghai) Co., Ltd."}]},{"given":"Feiyue","family":"Huang","sequence":"additional","affiliation":[{"name":"Tencent Youtu Lab, Tencent Technology (Shanghai) Co., Ltd."}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:49:33Z","timestamp":1564300173000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/471"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/471","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}