Zero-Shot Learning/Classification3 [논문리뷰] MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning MathJax = { tex: {inlineMath: [['$', '$'], ['\\(', '\\)']]} }; Paper OverviewCVPR'22https://openaccess.thecvf.com/content/CVPR2022/html/Chen_MSDN_Mutually_Semantic_Distillation_Network_for_Zero-Shot_Learning_CVPR_2022_paper.html CVPR 2022 Open Access RepositoryMSDN: Mutually Semantic Distillation Network for Zero-Shot Learning Shiming Chen, Ziming Hong, Guo-Sen Xie, Wenhan Yang, Qinmu .. 2024. 4. 22. [논문리뷰] FREE: Feature Refinement for Generalized Zero-Shot Learning Paper Overview ICCV'21 https://arxiv.org/abs/2107.13807 FREE: Feature Refinement for Generalized Zero-Shot Learning Generalized zero-shot learning (GZSL) has achieved significant progress, with many efforts dedicated to overcoming the problems of visual-semantic domain gap and seen-unseen bias. However, most existing methods directly use feature extraction models traine arxiv.org Abstract 대부분 존재.. 2024. 4. 11. [논문리뷰] HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning Paper Overview NeurIPS'21 https://arxiv.org/abs/2109.15163 HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring semantic knowledge from seen classes to unseen ones. Typically, to guarantee desirable knowledge transfer, a common (latent) space is adopted for associating the visual and arxiv.org Abs.. 2024. 2. 27. 이전 1 다음
