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To effectively capture this diverse motion pattern, this paper presents a new temporal adaptive module ({\\bf TAM}) to generate video-specific temporal kernels based on its own feature map. TAM proposes a unique two-level adaptive modeling scheme by decoupling the dynamic kernel into a location sensitive importance map and a location invariant aggregation weight. The importance map is learned in a local temporal window to capture short-term information, while the aggregation weight is generated from a global view with a focus on long-term structure. TAM is a modular block and could be integrated into 2D CNNs to yield a powerful video architecture (TANet) with a very small extra computational cost. The extensive experiments on Kinetics-400 and Something-Something datasets demonstrate that our TAM outperforms other temporal modeling methods consistently, and achieves the state-of-the-art performance under the similar complexity. The code is available at \\url{ https://github.com/liu-zhy/temporal-adaptive-module}.","url_abs":"https://arxiv.org/abs/2005.06803v3","url_pdf":"https://arxiv.org/pdf/2005.06803v3.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":"tam-temporal-adaptive-module-for-video","repo_url":"https://github.com/liu-zhy/TANet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"tam-temporal-adaptive-module-for-video","repo_url":"https://github.com/liu-zhy/temporal-adaptive-module","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"video-recognition","task_name":"Video Recognition"}],"methods":[{"method_slug":"speed","method_name":"SPEED"},{"method_slug":"tam","method_name":"TAM"}],"datasets_introduced":[],"methods_introduced":[{"slug":"tam","name":"TAM","full_name":"Temporal Adaptive Module"}],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2005.06803","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.06803"}},"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. 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