{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/discriminative-learning-of-latent-features","title":"Discriminative Learning of Latent Features for Zero-Shot Recognition","arxiv_id":"1803.06731","date":"2018-03-18","proceeding":"CVPR 2018 6","authors":["Yan Li","Junge Zhang","Jian-Guo Zhang","Kaiqi Huang"],"abstract":"Zero-shot learning (ZSL) aims to recognize unseen image categories by\nlearning an embedding space between image and semantic representations. For\nyears, among existing works, it has been the center task to learn the proper\nmapping matrices aligning the visual and semantic space, whilst the importance\nto learn discriminative representations for ZSL is ignored. In this work, we\nretrospect existing methods and demonstrate the necessity to learn\ndiscriminative representations for both visual and semantic instances of ZSL.\nWe propose an end-to-end network that is capable of 1) automatically\ndiscovering discriminative regions by a zoom network; and 2) learning\ndiscriminative semantic representations in an augmented space introduced for\nboth user-defined and latent attributes. Our proposed method is tested\nextensively on two challenging ZSL datasets, and the experiment results show\nthat the proposed method significantly outperforms state-of-the-art methods.","url_abs":"http://arxiv.org/abs/1803.06731v1","url_pdf":"http://arxiv.org/pdf/1803.06731v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"discriminative-learning-of-latent-features","repo_url":"https://github.com/MARMOTatZJU/ZSLPR-TIANCHI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"zero-shot-learning","task_name":"Zero-Shot Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.06731","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}