{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T11:44:56Z","timestamp":1762429496405},"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":[[2022,7]]},"abstract":"<jats:p>Generative methods have been successfully applied in zero-shot learning (ZSL) by learning an implicit mapping to alleviate the visual-semantic domain gaps and synthesizing unseen samples to handle the data imbalance between seen and unseen classes. However, existing generative methods simply use visual features extracted by the pre-trained CNN backbone. These visual features lack attribute-level semantic information. Consequently, seen classes are indistinguishable, and the knowledge transfer from seen to unseen classes is limited. To tackle this issue, we propose a novel Semantic Compression Embedding Guided Generation (SC-EGG) model, which cascades a semantic compression embedding network (SCEN) and an embedding guided generative network (EGGN). The SCEN extracts a group of attribute-level local features for each sample and further compresses them into the new low-dimension visual feature. Thus, a dense-semantic visual space is obtained. The EGGN learns a mapping from the class-level semantic space to the dense-semantic visual space, thus improving the discriminability of the synthesized dense-semantic unseen visual features. Extensive experiments on three benchmark datasets, i.e., CUB, SUN and AWA2, demonstrate the signi\ufb01cant performance gains of SC-EGG over current state-of-the-art methods and its baselines.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/134","type":"proceedings-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T02:55:56Z","timestamp":1657940156000},"page":"956-963","source":"Crossref","is-referenced-by-count":17,"title":["Semantic Compression Embedding for Generative Zero-Shot Learning"],"prefix":"10.24963","author":[{"given":"Ziming","family":"Hong","sequence":"first","affiliation":[{"name":"Huazhong University of Science and Technology"}]},{"given":"Shiming","family":"Chen","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology"}]},{"given":"Guo-Sen","family":"Xie","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology"}]},{"given":"Wenhan","family":"Yang","sequence":"additional","affiliation":[{"name":"Nanyang Technological University"}]},{"given":"Jian","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of North Electronic Equipment"}]},{"given":"Yuanjie","family":"Shao","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology"}]},{"given":"Qinmu","family":"Peng","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology"}]},{"given":"Xinge","family":"You","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology"}]}],"member":"10584","event":{"number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2022","name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","start":{"date-parts":[[2022,7,23]]},"theme":"Artificial Intelligence","location":"Vienna, Austria","end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:07:53Z","timestamp":1658142473000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/134"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/134","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}