← 研踪 PaperTrailCVF · CVPR 2026:cvf:6e65fc5a31b71c6cd8a497689f233f28 · 记录 #1
Agentic Video Summarization via Self-Reflecting Multimodal Understanding
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 03:35
摘要
The rise of AI agents powered by large language models (LLMs) has transformed intelligent systems by enabling autonomous tool utilizing, reasoning, and action across diverse tasks. Despite this rapid progress, existing video summarization approaches primarily focus on feature extraction or frame-level importance regression but lack the autonomous reasoning, self-correction, and decision-making capabilities that define true agent-based intelligence. To bridge this gap, we propose AgenticVS--the first agentic workflow for video summarization that leverages multimodal large language models (MLLMs) to complete the summarization-verify-reflection loop in a fully autonomous manner. Rather than designing new architectures for feature extraction or regression, we exploit the understanding and reflective reasoning abilities of MLLMs to build an adaptive summarization framework with a self-reflecting workflow. Experiments on SumMe and TVSum demonstrate that our agentic workflow outperforms state-of-the-art methods, enhancing interpretability, adaptability, and paving the way for agent-based multimodal video understanding.
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记录 #1 · 2026 · 最新观测版本
Agentic Video Summarization via Self-Reflecting Multimodal Understanding
The rise of AI agents powered by large language models (LLMs) has transformed intelligent systems by enabling autonomous tool utilizing, reasoning, and action across diverse tasks. Despite this rapid progress, existing video summarization approaches primarily focus on feature extraction or frame-level importance regression but lack the autonomous reasoning, self-correction, and decision-making capabilities that define true agent-based intelligence. To bridge this gap, we propose AgenticVS--the first agentic workflow for video summarization that leverages multimodal large language models (MLLMs) to complete the summarization-verify-reflection loop in a fully autonomous manner. Rather than designing new architectures for feature extraction or regression, we exploit the understanding and reflective reasoning abilities of MLLMs to build an adaptive summarization framework with a self-reflecting workflow. Experiments on SumMe and TVSum demonstrate that our agentic workflow outperforms state-of-the-art methods, enhancing interpretability, adaptability, and paving the way for agent-based multimodal video understanding.
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