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<p>Action anticipation, which aims to forecast future activities from partially observed sequences, plays a crucial role in advancing computer vision applications. Traditional methods primarily rely on visual cues, limiting their capability to capture long-term dependencies and contextual semantics. This paper introduces the Semantic-Guided Adaptive Fusion Transformer (SAFT), a novel framework that integrates visual and textual information through a Visual Transformer Anticipation Module, a Sequential Context Correction Module, and an Adaptive Fusion Control Module. Experimental results on benchmark datasets demonstrate SAFT’s superior performance, outperforming state-of-the-art methods in most experimental configurations.</p>

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Multimodal adaptive fusion for enhanced long-term action anticipation

  • 期刊论文

  • 发表日期：

    2025年12月13日

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  • Yaoyao Jing,
  • Yilong Xiao,
  • Haoming Fang,
  • Keming Mao,
  • Jianzhe Zhao,
  • Xinlu Xiao

摘要

Action anticipation, which aims to forecast future activities from partially observed sequences, plays a crucial role in advancing computer vision applications. Traditional methods primarily rely on visual cues, limiting their capability to capture long-term dependencies and contextual semantics. This paper introduces the Semantic-Guided Adaptive Fusion Transformer (SAFT), a novel framework that integrates visual and textual information through a Visual Transformer Anticipation Module, a Sequential Context Correction Module, and an Adaptive Fusion Control Module. Experimental results on benchmark datasets demonstrate SAFT’s superior performance, outperforming state-of-the-art methods in most experimental configurations.