VideoAgent: Long-form Video Understanding with Large Language Model as Agent
VideoAgent uses a large language model as an agent for multi-round retrieval, achieving 54.1% zero-shot accuracy with only 8.4 frames on average.
Key Findings
Methodology
The approach models long video understanding as an interactive process controlled by a large language model (e.g., GPT-4). It iteratively samples initial frames, uses CLIP for retrieval, and VLM for captioning, forming a state-action-observation loop. The model employs chain-of-thought prompting and self-reflection to decide whether enough information has been gathered, reducing the number of frames needed (average 8.4). This process emphasizes reasoning over direct long-sequence processing, enabling efficient and accurate understanding.
Key Results
- On EgoSchema, VideoAgent achieves 54.1% accuracy using only 8.4 frames, outperforming SOTA LLoVi (50.3%) by 3.8%. On NExT-QA, it reaches 71.3%, surpassing LLoVi (67.7%), with only 8.2 frames. The method demonstrates strong generalization to videos extending hours, validating multi-round reasoning’s effectiveness.
- Frame selection via iterative querying significantly improves efficiency, outperforming uniform sampling. The model performs better on temporal and causal questions, requiring fewer frames for complex reasoning tasks.
- Ablation studies confirm the importance of initial frame count, segment retrieval, and self-evaluation. The adaptive process reduces unnecessary data, maintaining high accuracy with minimal frames.
Significance
This work advances long video understanding by shifting focus from static feature extraction to reasoning-driven analysis. The agent-based framework mimics human cognition, enabling models to handle extended sequences efficiently. It addresses core challenges of computational cost and information overload, paving the way for scalable, real-world applications such as content moderation, video summarization, and intelligent surveillance. The approach’s efficiency and robustness mark a significant step forward in AI’s ability to interpret complex, lengthy videos.
Technical Contribution
The paper introduces a novel agent-based framework integrating GPT-4, CLIP, and VLMs for multi-round, decision-driven video understanding. It innovatively combines dynamic frame sampling, chain-of-thought reasoning, and self-reflection, reducing reliance on processing entire sequences. The system’s modular design allows flexible tool use, significantly improving efficiency and interpretability. Theoretical analysis supports the adaptive sampling strategy, and extensive experiments validate its superiority over existing methods.
Novelty
This is the first work to formulate long video understanding as a multi-round, agent-controlled decision process driven by a large language model. Unlike prior static or single-pass methods, it dynamically selects frames based on reasoning needs, mimicking human cognition. The integration of self-reflection and multi-tool collaboration (CLIP, VLM) represents a fundamental innovation, enabling high accuracy with minimal data and computation.
Limitations
- Despite its efficiency, the approach may still struggle with extremely noisy or highly ambiguous videos, where visual cues are insufficient or misleading. Its reliance on pre-trained models limits adaptability to unseen domains. Computational costs, though reduced, remain non-trivial for real-time applications. Further robustness and scalability improvements are needed for deployment in diverse real-world scenarios.
Future Work
Future directions include enhancing visual understanding models for more accurate captioning and retrieval, integrating reinforcement learning to optimize frame selection, and expanding multi-modal inputs like audio. Improving real-time inference and robustness in noisy environments will be key. Additionally, extending the framework to unsupervised or semi-supervised settings could broaden applicability.
AI Executive Summary
Understanding long videos remains a fundamental challenge in computer vision, especially given the high computational costs and information overload associated with processing extended sequences. Traditional models often rely on uniform sampling or compression techniques, which can either miss critical details or introduce noise, limiting their effectiveness. To address these issues, this work introduces VideoAgent, a novel system that leverages a large language model (such as GPT-4) as an interactive agent to mimic human-like reasoning in long video comprehension.
The core innovation lies in framing the understanding process as a multi-round decision-making loop. Initially, the system samples a few frames uniformly, converts visual content into textual descriptions using a vision-language model, and then employs chain-of-thought prompting and self-reflection to assess whether enough information has been gathered. If not, it directs a CLIP-based retrieval module to find relevant frames, which are then captioned and incorporated into the current state. This iterative process continues until the model confidently answers the question or reaches a maximum number of rounds.
Experimental results on the EgoSchema and NExT-QA benchmarks demonstrate the effectiveness of this approach. On EgoSchema, the system achieves 54.1% accuracy with only 8.4 frames on average, outperforming previous state-of-the-art methods like LLoVi by a significant margin. Similarly, on NExT-QA, it reaches 71.3%, with only 8.2 frames, showing superior efficiency and accuracy. These results highlight the potential of agent-based, reasoning-driven frameworks in long-form video understanding.
The method’s ability to select relevant frames adaptively reduces computational costs dramatically while maintaining high performance. Ablation studies confirm the importance of multi-round reasoning, initial frame sampling, and self-evaluation. Despite its success, challenges remain in handling extremely noisy videos and achieving real-time inference. Future work aims to improve visual understanding, incorporate multi-modal data, and optimize the system for practical deployment. Overall, VideoAgent marks a significant step toward intelligent, scalable long video comprehension, opening new avenues for research and application.
Deep Dive
Abstract
Long-form video understanding represents a significant challenge within computer vision, demanding a model capable of reasoning over long multi-modal sequences. Motivated by the human cognitive process for long-form video understanding, we emphasize interactive reasoning and planning over the ability to process lengthy visual inputs. We introduce a novel agent-based system, VideoAgent, that employs a large language model as a central agent to iteratively identify and compile crucial information to answer a question, with vision-language foundation models serving as tools to translate and retrieve visual information. Evaluated on the challenging EgoSchema and NExT-QA benchmarks, VideoAgent achieves 54.1% and 71.3% zero-shot accuracy with only 8.4 and 8.2 frames used on average. These results demonstrate superior effectiveness and efficiency of our method over the current state-of-the-art methods, highlighting the potential of agent-based approaches in advancing long-form video understanding.