{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:13:14Z","timestamp":1777705994333,"version":"3.51.4"},"reference-count":49,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,7,2]]},"abstract":"<jats:p>Zero-Shot Learning (ZSL) has made significant progress driven by deep learning and is being promoted further with the advent of generative models. Despite the success of these methods, the type and number of unseen categories are nailed in the generative models, which makes it challenging to recognize unseen categories in an incremental manner, and the profits of some superior performance algorithms largely arise from their advanced capability of feature extraction, such as Transformers. This paper rigidly follows the assumptions introduced in conventional ZSL and proposes a visual feature filtering method based on a semantic mapping model, namely, filtering visual features through class-specific filters to effectively remove class-agnostic information. Extensive experiments are conducted on four benchmark datasets and have achieved very competitive performance.<\/jats:p>","DOI":"10.3233\/jifs-224297","type":"journal-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T12:15:17Z","timestamp":1683288917000},"page":"563-576","source":"Crossref","is-referenced-by-count":1,"title":["Boosting generalized zero-shot learning with category-specific filters"],"prefix":"10.1177","volume":"45","author":[{"given":"Ke","family":"Sun","sequence":"first","affiliation":[{"name":"Makarov College of Marine Engineering, Jiangsu Ocean University, Lianyungang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojie","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"He","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Data Link and Communication, Nanjing Research Institute of Electronic Engineering, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunyang","family":"Yan","sequence":"additional","affiliation":[{"name":"Makarov College of Marine Engineering, Jiangsu Ocean University, Lianyungang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haofeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-224297_ref1","first-page":"59","article-title":"Multi-cue zero-shot learning with strong supervision","author":"Akata","year":"2016","journal-title":"Proceedings of the IEEE conference on computer vision and pattern recognition"},{"key":"10.3233\/JIFS-224297_ref2","first-page":"819","article-title":"Label-embedding for attribute-based classification","author":"Akata","year":"2013","journal-title":"Proceedings of the IEEE conference on computer vision and pattern recognition"},{"key":"10.3233\/JIFS-224297_ref3","unstructured":"Alamri Faisal and Dutta Anjan, Multi-head self-attention via vision transformer for zero-shot learning, In Irish Machine Vision and Image Processing Conference, 2021."},{"key":"10.3233\/JIFS-224297_ref4","first-page":"7603","article-title":"Preserving semantic relations for zero-shot learning","author":"Annadani","year":"2018","journal-title":"Proceedings of the IEEE conference on computer vision and pattern recognition"},{"issue":"3","key":"10.3233\/JIFS-224297_ref5","doi-asserted-by":"crossref","first-page":"5159","DOI":"10.3233\/JIFS-201920","article-title":"Dual discriminative auto-encoder network for zero shot image recognition","volume":"40","author":"Bai","year":"2021","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"10.3233\/JIFS-224297_ref6","first-page":"10333","article-title":"Modeling inter and intra-class relations in the triplet loss for zero-shot learning","author":"Yannick","year":"2019","journal-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision"},{"key":"10.3233\/JIFS-224297_ref7","doi-asserted-by":"crossref","unstructured":"Chen Dubing, Shen Yuming, Zhang Haofeng and H.S. 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