Uncertainty-Aware and Decoder-Aligned Learning for Video Summarization
Video summarization aims to produce a compact representation of a long video by selecting a subset of temporally important segments that best reflect human preferences. This task is inherently difficult due to strong annotation subjectivity and the reliance on discrete decoding procedures, such as temporal segmentation and knapsack-based selection, during evaluation. Most existing approaches either learn deterministic importance scores that overlook these characteristics or adopt complex generative models that increase training and inference cost. In this paper, we propose VASTSum, an uncertainty-aware and decoder-aligned learning framework for video summarization that addresses both chal...
| Authors | Omer Tariq, Syed Muhammad Raza, Jeongbae Son |
|---|---|
| Primary category | cs.CV |
| Published | 10 May 2026 |
| Last updated at source | 10 May 2026 |
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