{"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/efficient-movie-scene-detection-using-state","title":"Efficient Movie Scene Detection using State-Space Transformers","arxiv_id":"2212.14427","date":"2022-12-29","proceeding":"CVPR 2023 1","authors":["Md Mohaiminul Islam","Mahmudul Hasan","Kishan Shamsundar Athrey","Tony Braskich","Gedas Bertasius"],"abstract":"The ability to distinguish between different movie scenes is critical for understanding the storyline of a movie. However, accurately detecting movie scenes is often challenging as it requires the ability to reason over very long movie segments. This is in contrast to most existing video recognition models, which are typically designed for short-range video analysis. This work proposes a State-Space Transformer model that can efficiently capture dependencies in long movie videos for accurate movie scene detection. Our model, dubbed TranS4mer, is built using a novel S4A building block, which combines the strengths of structured state-space sequence (S4) and self-attention (A) layers. Given a sequence of frames divided into movie shots (uninterrupted periods where the camera position does not change), the S4A block first applies self-attention to capture short-range intra-shot dependencies. Afterward, the state-space operation in the S4A block is used to aggregate long-range inter-shot cues. The final TranS4mer model, which can be trained end-to-end, is obtained by stacking the S4A blocks one after the other multiple times. Our proposed TranS4mer outperforms all prior methods in three movie scene detection datasets, including MovieNet, BBC, and OVSD, while also being $2\\times$ faster and requiring $3\\times$ less GPU memory than standard Transformer models. We will release our code and models.","url_abs":"https://arxiv.org/abs/2212.14427v2","url_pdf":"https://arxiv.org/pdf/2212.14427v2.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":"efficient-movie-scene-detection-using-state","repo_url":"https://github.com/md-mohaiminul/trans4mer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"scene-segmentation","task_name":"Scene Segmentation"},{"task_slug":"video-classification","task_name":"Video Classification"},{"task_slug":"video-recognition","task_name":"Video Recognition"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/scene-segmentation-on-movienet","task":"Scene Segmentation","dataset":"MovieNet","model":"TranS4mer","rank_in_archive_order":2,"of":2,"metrics":{"AP":"60.78"},"uses_additional_data":false},{"leaderboard":"/sota/video-classification-on-breakfast","task":"Video Classification","dataset":"Breakfast","model":"TranS4mer","rank_in_archive_order":4,"of":9,"metrics":{"Accuracy (%)":"90.27"},"uses_additional_data":false},{"leaderboard":"/sota/video-classification-on-coin-1","task":"Video Classification","dataset":"COIN","model":"TranS4mer","rank_in_archive_order":5,"of":7,"metrics":{"Accuracy (%)":"89.3"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2212.14427","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.14427"}},"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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