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SEAL-lvu SEAL: SEmantic Attention Learning for Long Video Representation Lan Wang 1,2 Yujia Chen 2 Du Tran 2 Vishnu Boddeti 1 Wen-Sheng Chu 2 1 Michigan State University   2 Google CVPR 2025 (Oral: 0.7% acceptance ratio) [Paper] [ArXiv] [Code (coming soon)] Long Video Representation with Semantic Attention Learning (SEAL): Conventional uniform sampling results in redundant and cluttered visual information, making it difficult for both AI models and human brains to process efficiently. Decomposing long videos into semantic entities such as scenes, objects, and actions reduces temporal redundancy, thus making model training and inference more efficient. In this example, the long video 𝒱 is decomposed into 4 scene tokens (S1--S4), 6 object tokens (O1--O6), and 4 action/event tokens (A1--A4). A query-aware attention learning module improves downstream task performance by focusing on relevant information rather ...
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