SeViCES: Unifying Semantic-Visual Evidence Consensus for Long Video Understanding

• URL: http://arxiv.org/abs/2510.20622v1
• Date: Thu, 23 Oct 2025 14:55:28 GMT
• Title: SeViCES: Unifying Semantic-Visual Evidence Consensus for Long Video Understanding
• Authors: Yuan Sheng, Yanbin Hao, Chenxu Li, Shuo Wang, Xiangnan He,
• Abstract summary: We propose a framework for effective and reliable long video understanding.<n>SeViCES is training-free and model-agnostic, and introduces two key components.<n> experiments on long video understanding benchmarks show that SeViCES consistently outperforms state-of-the-art methods in both accuracy and robustness.
• Score: 36.30263540665245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long video understanding remains challenging due to its complex, diverse, and temporally scattered content. Although video large language models (Video-LLMs) can process videos lasting tens of minutes, applying them to truly long sequences is computationally prohibitive and often leads to unfocused or inconsistent reasoning. A promising solution is to select only the most informative frames, yet existing approaches typically ignore temporal dependencies or rely on unimodal evidence, limiting their ability to provide complete and query-relevant context. We propose a Semantic-Visual Consensus Evidence Selection (SeViCES) framework for effective and reliable long video understanding. SeViCES is training-free and model-agnostic, and introduces two key components. The Semantic-Visual Consensus Frame Selection (SVCFS) module selects frames through (1) a temporal-aware semantic branch that leverages LLM reasoning over captions, and (2) a cluster-guided visual branch that aligns embeddings with semantic scores via mutual information. The Answer Consensus Refinement (ACR) module further resolves inconsistencies between semantic- and visual-based predictions by fusing evidence and constraining the answer space. Extensive experiments on long video understanding benchmarks show that SeViCES consistently outperforms state-of-the-art methods in both accuracy and robustness, demonstrating the importance of consensus-driven evidence selection for Video-LLMs.

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