Visual Context Window Extension: A New Perspective for Long Video Understanding

TL;DR

This paper proposes extending the visual context window in large multimodal models to improve long video understanding without retraining, using a progressive pooling strategy to reduce memory consumption and enhance performance.

Contribution

The authors introduce a novel approach to adapt LMMs for long videos by extending visual context windows without retraining, addressing modality discrepancies and memory challenges.

Findings

Outperforms GPT-4o on MLVU benchmark with only 7B parameters.

Reduces memory usage by approximately 45% in 256-frame setting.

Consistently improves performance with increasing video frames.

Abstract

Large Multimodal Models (LMMs) have demonstrated impressive performance in short video understanding tasks but face great challenges when applied to long video understanding. In contrast, Large Language Models (LLMs) exhibit outstanding capabilities in modeling long texts. Existing work attempts to address this issue by introducing long video-text pairs during training. However, these approaches require substantial computational and data resources. In this paper, we tackle the challenge of long video understanding from the perspective of context windows, aiming to apply LMMs to long video tasks without retraining on long video datasets. We first conduct an in-depth analysis of why pretrained LMMs struggle to understand lengthy video content, identifying that discrepancies between visual and language modalities lead to different context windows for visual and language tokens, making it difficult to directly extend the visual tokens to match the language context window. Based on this, we propose to adapt LMMs for long video understanding tasks by extending the visual context window, eliminating the need for retraining on large scalelong video datasets. To further mitigate the significant memory consumption caused by long sequences, we introduce a progressive pooling inference strategy that selectively adjusts the spatial resolution of frame embeddings, reducing the number of visual tokens while retaining important spatial information. Across multiple long video understanding benchmarks, our method consistently improves the performance as the number of video frames increases. On the MLVU benchmark, our method outperforms GPT-4o, even though our model size is only 7B. Additionally, in the 256-frame setting, our method reduces memory usage by approximately 45% compared to the baseline, without introducing any performance loss.