{"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/look-more-but-care-less-in-video-recognition","title":"Look More but Care Less in Video Recognition","arxiv_id":"2211.09992","date":"2022-11-18","proceeding":null,"authors":["Yitian Zhang","Yue Bai","Huan Wang","Yi Xu","Yun Fu"],"abstract":"Existing action recognition methods typically sample a few frames to represent each video to avoid the enormous computation, which often limits the recognition performance. To tackle this problem, we propose Ample and Focal Network (AFNet), which is composed of two branches to utilize more frames but with less computation. Specifically, the Ample Branch takes all input frames to obtain abundant information with condensed computation and provides the guidance for Focal Branch by the proposed Navigation Module; the Focal Branch squeezes the temporal size to only focus on the salient frames at each convolution block; in the end, the results of two branches are adaptively fused to prevent the loss of information. With this design, we can introduce more frames to the network but cost less computation. Besides, we demonstrate AFNet can utilize fewer frames while achieving higher accuracy as the dynamic selection in intermediate features enforces implicit temporal modeling. Further, we show that our method can be extended to reduce spatial redundancy with even less cost. Extensive experiments on five datasets demonstrate the effectiveness and efficiency of our method.","url_abs":"https://arxiv.org/abs/2211.09992v1","url_pdf":"https://arxiv.org/pdf/2211.09992v1.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":"look-more-but-care-less-in-video-recognition","repo_url":"https://github.com/bespontaneous/afnet-pytorch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"video-recognition","task_name":"Video Recognition"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2211.09992","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.09992"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/BeSpontaneous/AFNet-pytorch","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/bespontaneous/afnet-pytorch","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":4,"unverified":3},"by_repo_kind":{"official":{"samples":7,"ran":4,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"722eaac491177e21","entry":"Bottleneck_ample","repo":"bespontaneous/afnet-pytorch","repo_kind":"official","path":"ops/backbone/AF_ResNet.py","file_url":"https://github.com/bespontaneous/afnet-pytorch/blob/HEAD/ops/backbone/AF_ResNet.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"722eaac491177e21"}},{"code_sha256_prefix":"f6de5e19425f73d6","entry":"GumbleSoftmax","repo":"bespontaneous/afnet-pytorch","repo_kind":"official","path":"ops/backbone/AF_ResNet.py","file_url":"https://github.com/bespontaneous/afnet-pytorch/blob/HEAD/ops/backbone/AF_ResNet.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f6de5e19425f73d6"}},{"code_sha256_prefix":"05db6c4ace48af9a","entry":"dynamic_fusion","repo":"bespontaneous/afnet-pytorch","repo_kind":"official","path":"ops/backbone/AF_ResNet.py","file_url":"https://github.com/bespontaneous/afnet-pytorch/blob/HEAD/ops/backbone/AF_ResNet.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"05db6c4ace48af9a"}},{"code_sha256_prefix":"5a5b1b97c63d9eb6","entry":"navigation","repo":"bespontaneous/afnet-pytorch","repo_kind":"official","path":"ops/backbone/AF_ResNet.py","file_url":"https://github.com/bespontaneous/afnet-pytorch/blob/HEAD/ops/backbone/AF_ResNet.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5a5b1b97c63d9eb6"}},{"code_sha256_prefix":"5d8c29317a714dd1","entry":"AFModule","repo":"bespontaneous/afnet-pytorch","repo_kind":"official","path":"ops/backbone/AF_ResNet.py","file_url":"https://github.com/bespontaneous/afnet-pytorch/blob/HEAD/ops/backbone/AF_ResNet.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5d8c29317a714dd1"}},{"code_sha256_prefix":"1b8764ca6239ee81","entry":"AFResNet","repo":"bespontaneous/afnet-pytorch","repo_kind":"official","path":"ops/backbone/AF_ResNet.py","file_url":"https://github.com/bespontaneous/afnet-pytorch/blob/HEAD/ops/backbone/AF_ResNet.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1b8764ca6239ee81"}},{"code_sha256_prefix":"6789d3209771b4e6","entry":"TSM","repo":"bespontaneous/afnet-pytorch","repo_kind":"official","path":"ops/backbone/AF_ResNet.py","file_url":"https://github.com/bespontaneous/afnet-pytorch/blob/HEAD/ops/backbone/AF_ResNet.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6789d3209771b4e6"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}