{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T21:39:55Z","timestamp":1767994795446,"version":"3.49.0"},"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>Deep learning has achieved remarkable results in 3D shape analysis by learning global shape features from the pixel-level over multiple views. Previous methods, however, compute low-level features for entire views without considering part-level information. In contrast, we propose a deep neural network, called Parts4Feature, to learn 3D global features from part-level information in multiple views. We introduce a novel definition of generally semantic parts, which Parts4Feature learns to detect in multiple views from different 3D shape segmentation benchmarks. A key idea of our architecture is that it transfers the ability to detect semantically meaningful parts in multiple views to learn 3D global features. Parts4Feature achieves this by combining a local part detection branch and a global feature learning branch with a shared region proposal module. The global feature learning branch aggregates the detected parts in terms of learned part patterns with a novel multi-attention mechanism, while the region proposal module enables locally and globally discriminative information to be promoted by each other. We demonstrate that Parts4Feature outperforms the state-of-the-art under three large-scale 3D shape benchmarks.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/108","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"766-773","source":"Crossref","is-referenced-by-count":32,"title":["Parts4Feature: Learning 3D Global Features from Generally Semantic Parts in Multiple Views"],"prefix":"10.24963","author":[{"given":"Zhizhong","family":"Han","sequence":"first","affiliation":[{"name":"School of Software, Tsinghua University, Beijing, China"},{"name":"Department of Computer Science, University of Maryland, College Park, USA"}]},{"given":"Xinhai","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Software, Tsinghua University, Beijing, China"},{"name":"Beijing National Research Center for Information Science and Technology (BNRist)"}]},{"given":"Yu-Shen","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Software, Tsinghua University, Beijing, China"},{"name":"Beijing National Research Center for Information Science and Technology (BNRist)"}]},{"given":"Matthias","family":"Zwicker","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Maryland, College Park, USA"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"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":"2019-07-28T03:46:50Z","timestamp":1564285610000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/108"}},"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\/108","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}