EEA: Exploration-Exploitation Agent for Long Video Understanding

2025-12-03

AI Summary

Long-video understanding faces dual challenges of information sparsity and prohibitive computational cost: dense processing is inefficient, while coarse-grained strategies often miss critical frames. To address this, we propose a semantic-guided hierarchical tree search framework that dynamically balances exploration (covering unseen segments) and exploitation (focusing on salient semantic cues) via an adaptive mechanism integrating intrinsic rewards and semantic priors. Our method jointly leverages a hierarchical tree structure, vision-language model (VLM)-driven intrinsic reward, semantic anchor matching, and uncertainty-aware pruning—where semantic queries are updated online and epistemic uncertainty is explicitly modeled. Evaluated on multiple long-video understanding benchmarks, our approach achieves state-of-the-art performance at significantly reduced computational cost, demonstrating superior accuracy, strong generalization across domains, and high inference efficiency.

Technology Category

• Search and Optimization: Learning to Search
• Computer Vision: Video Understanding & Activity Analysis
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Long-form video understanding requires efficient navigation of extensive visual data to pinpoint sparse yet critical information. Current approaches to longform video understanding either suffer from severe computational overhead due to dense preprocessing, or fail to effectively balance exploration and exploitation, resulting in incomplete information coverage and inefficiency. In this work, we introduce EEA, a novel video agent framework that archives exploration-exploitation balance through semantic guidance with hierarchical tree search process. EEA autonomously discovers and dynamically updates task-relevant semantic queries, and collects video frames closely matched to these queries as semantic anchors. During the tree search process, instead of uniform expansion, EEA preferentially explores semantically relevant frames while ensuring sufficient coverage within unknown segments. Moreover, EEA adaptively combines intrinsic rewards from visionlanguage models (VLMs) with semantic priors by explicitly modeling uncertainty to achieve stable and precise evaluation of video segments. Experiments across various long-video benchmarks validate the superior performance and computational efficiency of our proposed method.

Problem

• Balances exploration and exploitation in long video analysis
• Reduces computational overhead in processing extensive visual data
• Improves information coverage and efficiency in video understanding

Innovation

• Hierarchical tree search balances exploration and exploitation
• Dynamic semantic queries guide frame selection as anchors
• Uncertainty modeling combines vision-language rewards with priors