VideoAgent: Long-form Video Understanding with Large Language Model as Agent

핵심 개념

VideoAgent utilizes a large language model as an agent to iteratively identify and compile crucial information from long-form videos, showcasing superior effectiveness and efficiency in advancing video understanding.

초록

VideoAgent introduces an agent-based system for long-form video understanding, emphasizing interactive reasoning over direct processing of visual inputs. It employs a large language model to control the iterative process, utilizing vision-language models for translation and retrieval. The method achieves high accuracy on challenging benchmarks with minimal frames used. Differentiates from existing methods by multi-round frame selection and query rewriting for accurate retrieval.

통계

VideoAgent achieves 54.1% zero-shot accuracy on EgoSchema benchmark.
VideoAgent achieves 71.3% zero-shot accuracy on NExT-QA benchmark.
VideoAgent uses only 8.4 frames on average for analysis.

인용구

"Do we really need to feed the entire long-form video directly into the model?" - Content
"Our work differs from previous works in two aspects." - Content
"In summary, VideoAgent represents a significant stride for long-form video understanding." - Content

핵심 통찰 요약

VideoAgent

by Xiaohan Wang... 게시일 arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.10517.pdf

더 깊은 질문

How can the concept of interactive reasoning be applied to other domains beyond computer vision?

Interactive reasoning, as demonstrated in VideoAgent for long-form video understanding, can be applied to various domains beyond computer vision. In natural language processing, interactive reasoning could enhance chatbots' capabilities by allowing them to engage in more dynamic and context-aware conversations with users. In healthcare, interactive reasoning could assist medical professionals in diagnosing complex cases by iteratively gathering relevant information and making informed decisions. In finance, interactive reasoning could improve risk assessment models by dynamically adjusting strategies based on changing market conditions. Overall, the concept of interactive reasoning has broad applications across different fields where complex decision-making processes are involved.

What potential limitations or biases could arise from relying heavily on large language models like GPT-4 in video understanding?

Relying heavily on large language models like GPT-4 for video understanding may introduce several limitations and biases. One limitation is the model's reliance on textual descriptions generated by visual language models (VLMs) for interpreting visual content accurately. This dependency may lead to inaccuracies or misinterpretations if the VLM produces incorrect captions or fails to capture nuanced visual details effectively.
Another limitation is the model's susceptibility to bias present in training data used to pretrain these large language models. Biases related to gender, race, or cultural stereotypes present in the data can propagate through the model's predictions and affect its performance when analyzing videos with diverse content.
Additionally, there might be a limitation regarding computational resources required for running such large-scale models efficiently during real-time video analysis tasks. The complexity of GPT-4 may result in longer inference times and higher computational costs compared to smaller models.

How might the iterative frame selection process of VideoAgent be adapted for real-time applications or live streaming scenarios?

Adapting VideoAgent's iterative frame selection process for real-time applications or live streaming scenarios would require optimizing it for efficiency and speed without compromising accuracy.
One approach could involve implementing parallel processing techniques that allow multiple frames to be analyzed simultaneously during each iteration. This would help reduce latency and enable faster decision-making based on incoming video streams.
Furthermore, incorporating incremental learning mechanisms into VideoAgent could facilitate continuous adaptation and refinement of frame selection strategies over time as new information becomes available.
Moreover, leveraging hardware acceleration technologies such as GPUs or TPUs can significantly enhance the processing speed of VideoAgent during real-time operations.
Overall, adapting VideoAgent's iterative frame selection process for real-time applications would involve a combination of algorithmic optimizations and technological advancements aimed at achieving rapid yet accurate analysis of streaming video data streams.