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Summarized knowledge guidance for single-frame temporal action localization
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AI
DOI：10.1016/j.patrec.2025.02.027.png)
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摘要
En 中文
Single-frame temporal action localization has garnered attention in the computer vision community. Existing methods address annotation sparsity by generating dense pseudo labels within individual videos, but disregard the variable representation from intra-class action instances, resulting in inferior completeness localization. In this paper, we propose to model intra-class relationships by using Summarized Knowledge Guidance (SKG). Specifically, we initially design a learnable memory bank to summarize annotated single-frame knowledge for each class. Then, we introduce two corresponding components, i.e., the knowledge propagation module (KPM) and the knowledge refinement module (KRM), for intra-class guidance. In KPM, we propagate summarized knowledge for feature-level enhancement through bipartite matching. In KRM, summarized knowledge is presented as confident pseudo positive samples for label-level refinement in a contrastive learning manner. Extensive experiments and ablation studies on the THUMOS14, GTEA and BEOID reveal that our method significantly outperforms state-of-the-art methods.
Keyword:
Temporal action localization
Single-frame annotation
Memory bank
Contrastive learning
