{"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/collect-and-select-semantic-alignment-metric","title":"Collect and Select: Semantic Alignment Metric Learning for Few-Shot Learning","arxiv_id":null,"date":"2019-10-01","proceeding":"ICCV 2019 10","authors":["Fusheng Hao"," Fengxiang He"," Jun Cheng"," Lei Wang"," Jianzhong Cao"," Dacheng Tao"],"abstract":"Few-shot learning aims to learn latent patterns from few training examples and has shown promises in practice. However, directly calculating the distances between the query image and support image in existing methods may cause ambiguity because dominant objects can locate anywhere on images. To address this issue, this paper proposes a Semantic Alignment Metric Learning (SAML) method for few-shot learning that aligns the semantically relevant dominant objects through a \"collect-and-select\" strategy. Specifically, we first calculate a relation matrix (RM) to \"collect\" the distances of each local region pairs of the 3D tensor extracted from a query image and the mean tensor of the support images. Then, the attention technique is adapted to \"select\" the semantically relevant pairs and put more weights on them. Afterwards, a multi-layer perceptron (MLP) is utilized to map the reweighted RMs to their corresponding similarity scores. Theoretical analysis demonstrates the generalization ability of SAML and gives a theoretical guarantee. Empirical results demonstrate that semantic alignment is achieved. Extensive experiments on benchmark datasets validate the strengths of the proposed approach and demonstrate that SAML significantly outperforms the current state-of-the-art methods. The source code is available at https://github.com/haofusheng/SAML.\r","url_abs":"http://openaccess.thecvf.com/content_ICCV_2019/html/Hao_Collect_and_Select_Semantic_Alignment_Metric_Learning_for_Few-Shot_Learning_ICCV_2019_paper.html","url_pdf":"http://openaccess.thecvf.com/content_ICCV_2019/papers/Hao_Collect_and_Select_Semantic_Alignment_Metric_Learning_for_Few-Shot_Learning_ICCV_2019_paper.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":"collect-and-select-semantic-alignment-metric","repo_url":"https://github.com/haofusheng/SAML","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"metric-learning","task_name":"Metric Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}