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The short-form videos have explosive popularity and have dominated the newsocial media trends. Prevailing short-video platforms,~\textit{e.g.}, Kuaishou(Kwai), TikTok, Instagram Reels, and YouTube Shorts, have changed the way weconsume and create content. For video content creation and understanding, theshot boundary detection (SBD) is one of the most essential components invarious scenarios. In this work, we release a new public Short video sHotbOundary deTection dataset, named SHOT, consisting of 853 complete short videosand 11,606 shot annotations, with 2,716 high quality shot boundary annotationsin 200 test videos. Leveraging this new data wealth, we propose to optimize themodel design for video SBD, by conducting neural architecture search in asearch space encapsulating various advanced 3D ConvNets and Transformers. Ourproposed approach, named AutoShot, achieves higher F1 scores than previousstate-of-the-art approaches, e.g., outperforming TransNetV2 by 4.2%, when beingderived and evaluated on our newly constructed SHOT dataset. Moreover, tovalidate the generalizability of the AutoShot architecture, we directlyevaluate it on another three public datasets: ClipShots, BBC and RAI, and theF1 scores of AutoShot outperform previous state-of-the-art approaches by 1.1%,0.9% and 1.2%, respectively. The SHOT dataset and code can be found inhttps://github.com/wentaozhu/AutoShot.git .

AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection