{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:36:01Z","timestamp":1723016161097},"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>In this work, we tackle the zero-shot metric learning problem and propose a novel method abbreviated as ZSML, with the purpose to learn a distance metric that measures the similarity of unseen categories (even unseen datasets). ZSML achieves strong transferability by capturing multi-nonlinear yet continuous relation among data. It is motivated by two facts: 1) relations can be essentially described from various perspectives; and 2) traditional binary supervision is insufficient to represent continuous visual similarity. Specifically, we first reformulate a collection of specific-shaped convolutional kernels to combine data pairs and generate multiple relation vectors. Furthermore, we design a new cross-update regression loss to discover continuous similarity. Extensive experiments including intra-dataset transfer and inter-dataset transfer on four benchmark datasets demonstrate that ZSML can achieve state-of-the-art performance.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/555","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"3996-4002","source":"Crossref","is-referenced-by-count":7,"title":["Zero-shot Metric Learning"],"prefix":"10.24963","author":[{"given":"Xinyi","family":"Xu","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xian 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huanhuan","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xian 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanhua","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xian 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erkun","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xian 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xian 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"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":"2019-07-28T03:50:06Z","timestamp":1564285806000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/555"}},"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\/555","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}