{"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/selective-sparse-sampling-for-fine-grained","title":"Selective Sparse Sampling for Fine-Grained Image Recognition","arxiv_id":null,"date":"2019-10-01","proceeding":"ICCV 2019 10","authors":["Yao Ding"," Yanzhao Zhou"," Yi Zhu"," Qixiang Ye"," Jianbin Jiao"],"abstract":"Fine-grained recognition poses the unique challenge of capturing subtle inter-class differences under considerable intra-class variances (e.g., beaks for bird species). Conventional approaches crop local regions and learn detailed representation from those regions, but suffer from the fixed number of parts and missing of surrounding context. In this paper, we propose a simple yet effective framework, called Selective Sparse Sampling, to capture diverse and fine-grained details. The framework is implemented using Convolutional Neural Networks, referred to as Selective Sparse Sampling Networks (S3Ns). With image-level supervision, S3Ns collect peaks, i.e., local maximums, from class response maps to estimate informative, receptive fields and learn a set of sparse attention for capturing fine-detailed visual evidence as well as preserving context. The evidence is selectively sampled to extract discriminative and complementary features, which significantly enrich the learned representation and guide the network to discover more subtle cues. Extensive experiments and ablation studies show that the proposed method consistently outperforms the state-of-the-art methods on challenging benchmarks including CUB-200-2011, FGVC-Aircraft, and Stanford Cars.\r","url_abs":"http://openaccess.thecvf.com/content_ICCV_2019/html/Ding_Selective_Sparse_Sampling_for_Fine-Grained_Image_Recognition_ICCV_2019_paper.html","url_pdf":"http://openaccess.thecvf.com/content_ICCV_2019/papers/Ding_Selective_Sparse_Sampling_for_Fine-Grained_Image_Recognition_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":"selective-sparse-sampling-for-fine-grained","repo_url":"https://github.com/Yao-DD/S3N","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"fine-grained-image-classification","task_name":"Fine-Grained Image Classification"},{"task_slug":"fine-grained-image-recognition","task_name":"Fine-Grained Image Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/fine-grained-image-classification-on-fgvc","task":"Fine-Grained Image Classification","dataset":"FGVC Aircraft","model":"S3N","rank_in_archive_order":35,"of":57,"metrics":{"Accuracy":"92.8%"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-image-classification-on-stanford","task":"Fine-Grained Image Classification","dataset":"Stanford Cars","model":"S3N","rank_in_archive_order":34,"of":83,"metrics":{"Accuracy":"94.7%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}