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EIKA: Explicit & Implicit Knowledge-Augmented Network for entity-aware sports video captioning
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AI
DOI：10.1016/j.eswa.2025.126906.png)
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Abstract
En 中文
Sports video captioning in real application scenarios requires both entities and specific scenes. However, it is difficult to extract this fine-grained information solely from the video content. This paper introduces an Explicit & Implicit Knowledge-Augmented Network for Entity-Aware Sports Video Captioning (EIKA), which leverages both explicit game-related knowledge (i.e., the set of involved player entities) and implicit visual scene knowledge extracted from the training set. Our innovative Entity-Video Interaction Module (EVIM) and Video-Knowledge Interaction Module (VKIM) are instrumental in enhancing the extraction of entity-related and scene-specific video features, respectively. The spatiotemporal information in video is encoded by introducing the Spatial-Temporal Modeling Module (STMM). And the designed Scene-To-Entity (STE) decoder fully utilizes the two kinds of knowledge to generate informative captions with the distributed decoding approach. Extensive evaluations on the VC-NBA-2022, Goal and NSVA datasets demonstrate that our method has the leading performance compared with existing methods.
Keywords:
Entity-aware sports video captioning
Explicit knowledge
Implicit knowledge
Journal
IF:
7.5
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2.9W
Citations:
10.2W
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