Temporal Attention Alignment for Action Recognition Under Viewpoint Shifts
Keywords:
Action Recognition, Temporal Attention, Viewpoint Alignment, Video AnalysisAbstract
Human action recognition is a foundational task in computer vision with widespread applications in automated surveillance, human-computer interaction, and autonomous navigation. Despite significant advancements driven by deep learning architectures, recognizing actions across varying camera viewpoints remains a formidable challenge. The primary difficulty arises from severe visual occlusions, scale variations, and dramatic changes in the spatiotemporal appearance of actions when observed from different angles. Traditional methodologies predominantly focus on extracting viewpoint-invariant spatial features, often neglecting the crucial temporal dynamics that characterize human motion. This paper proposes a comprehensive framework centered on temporal attention alignment to address the cross-view action recognition problem. By conceptualizing video sequences as dynamic information streams, we postulate that human actions manifest distinct, yet theoretically alignable, temporal attention profiles regardless of the observer's vantage point. Our research introduces a novel methodology that aligns the temporal attention distributions of corresponding actions captured from disparate views, compelling the underlying neural network to focus on intrinsically meaningful motion phases rather than view-specific spatial artifacts. Through rigorous theoretical analysis and systematic empirical evaluation, this study demonstrates that enforcing consistency in temporal attention significantly enhances the robustness and generalization capabilities of action recognition models under viewpoint shifts. The findings indicate that temporal alignment acts as a critical regularization mechanism, offering a promising trajectory for developing highly invariant video understanding systems.
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Copyright (c) 2026 Guang Feng, Kevin Lau, Shan Yu
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