{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:37:21Z","timestamp":1723016241725},"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":[[2021,8]]},"abstract":"<jats:p>We propose an end-to-end-trainable feature augmentation module built for image classification that extracts and exploits multi-view local features to boost model performance. Different from using global average pooling (GAP) to extract vectorized features from only the global view, we propose to sample and ensemble diverse multi-view local features to improve model robustness. To sample class-representative local features, we incorporate a simple auxiliary classifier head (comprising only one 1x1 convolutional layer) which efficiently and adaptively attends to class-discriminative local regions of feature maps via our proposed AdaCAM (Adaptive Class Activation Mapping). Extensive experiments demonstrate consistent and noticeable performance gains achieved by our multi-view feature augmentation module.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/94","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:00:49Z","timestamp":1628679649000},"page":"678-684","source":"Crossref","is-referenced-by-count":1,"title":["Multi-view Feature Augmentation with Adaptive Class Activation Mapping"],"prefix":"10.24963","author":[{"given":"Xiang","family":"Gao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, University of Chinese Academy of Sciences"},{"name":"Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences"},{"name":"Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences"}]},{"given":"Yingjie","family":"Tian","sequence":"additional","affiliation":[{"name":"Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences"},{"name":"Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences"}]},{"given":"Zhiquan","family":"Qi","sequence":"additional","affiliation":[{"name":"Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences"},{"name":"Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences"}]}],"member":"10584","event":{"number":"30","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2021","name":"Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}","start":{"date-parts":[[2021,8,19]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2021,8,27]]}},"container-title":["Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:01:20Z","timestamp":1628679680000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/94"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2021\/94","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}