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title
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2026-02-12 14:00:13
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Feint6K Rethinking Video-Text Understanding: Retrieval from Counterfactually Augmented Data ECCV 2024 Wufei Ma 1,2 Kai Li 1 Zhongshi Jiang 1 Moustafa Meshry 1 Qihao Liu 2 Huiyu Wang 3 Christian Häne 1 Alan Yuille 2 1 Meta Reality Labs 2 Johns Hopkins University 3 Meta AI arXiv Data Contents RCAD and Feint6K Key Findings LLM-Teacher Miscellaneous We propose a novel evaluation task for video-text understanding, namely retrieval from counterfactually augmented data (RCAD), and a new Feint6K dataset, to better assess the capabilities of current video-text models and understand their limitations. To succeed on our new evaluation task, models must derive a comprehensive understanding of the video from cross-frame reasoning. Analyses show that previous video-text foundation models can be easily fooled by counterfactually augmented data and are far behind human-level performance. From our experiments on RCA...
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