{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T19:23:37Z","timestamp":1710271417494},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"01","license":[{"start":{"date-parts":[[2019,7,17]],"date-time":"2019-07-17T00:00:00Z","timestamp":1563321600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.aaai.org"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Person re-identification (Re-ID) is typically cast as the problem of semantic representation and alignment, which requires precisely discovering and modeling the inherent spatial structure information on person images. Motivated by this observation, we propose a Key-Value Memory Matching Network (KVM-MN) model that consists of key-value memory representation and key-value co-attention matching. The proposed KVM-MN model is capable of building an effective local-position-aware person representation that encodes the spatial feature information in the form of multi-head key-value memory. Furthermore, the proposed KVM-MN model makes use of multi-head co-attention to automatically learn a number of cross-person-matching patterns, resulting in more robust and interpretable matching results. Finally, we build a setwise learning mechanism that implements a more generalized query-to-gallery-image-set learning procedure. Experimental results demonstrate the effectiveness of the proposed model against the state-of-the-art.<\/jats:p>","DOI":"10.1609\/aaai.v33i01.33019235","type":"journal-article","created":{"date-parts":[[2019,8,19]],"date-time":"2019-08-19T07:32:34Z","timestamp":1566199954000},"page":"9235-9242","source":"Crossref","is-referenced-by-count":6,"title":["Learning a Key-Value Memory Co-Attention Matching Network for Person Re-Identification"],"prefix":"10.1609","volume":"33","author":[{"given":"Yaqing","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongfei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2019,7,17]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/4959\/4832","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/4959\/4832","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T07:03:05Z","timestamp":1667804585000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/4959"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,17]]},"references-count":0,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2019,7,23]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v33i01.33019235","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2019,7,17]]}}}