ViSIL: Unified Evaluation of Information Loss in Multimodal Video Captioning

• URL: http://arxiv.org/abs/2601.09851v1
• Date: Wed, 14 Jan 2026 20:14:47 GMT
• Title: ViSIL: Unified Evaluation of Information Loss in Multimodal Video Captioning
• Authors: Po-han Li, Shenghui Chen, Ufuk Topcu, Sandeep Chinchali,
• Abstract summary: Video Summary Information Loss (ViSIL) score is an information-theoretic framework that quantifies the video information not captured by a summary via vision-language model (VLM) inference model.<n>Our results demonstrate that ViSIL scores show a statistically significant correlation with both human and VLM performance on Video Question Answering tasks.
• Score: 23.144642468756032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal video captioning condenses dense footage into a structured format of keyframes and natural language. By creating a cohesive multimodal summary, this approach anchors generative AI in rich semantic evidence and serves as a lightweight proxy for high-efficiency retrieval. However, traditional metrics like BLEU or ROUGE fail to quantify information coverage across disparate modalities, such as comparing a paragraph of text to a sequence of keyframes. To address this, we propose the Video Summary Information Loss (ViSIL) score, an information-theoretic framework that quantifies the video information not captured by a summary via vision-language model (VLM) inference. By measuring the information loss, ViSIL is a unified metric that enables direct comparison across multimodal summary formats despite their structural discrepancies. Our results demonstrate that ViSIL scores show a statistically significant correlation with both human and VLM performance on Video Question Answering (VQA) tasks. ViSIL also enables summary selection to optimize the trade-off between information loss and processing speed, establishing a Pareto-optimal frontier that outperforms text summaries by $7\%$ in VQA accuracy without increasing processing load.

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