LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding (2410.17434v1)

Abstract: Multimodal LLMs (MLLMs) have shown promising progress in understanding and analyzing video content. However, processing long videos remains a significant challenge constrained by LLM's context size. To address this limitation, we propose LongVU, a spatiotemporal adaptive compression mechanism thats reduces the number of video tokens while preserving visual details of long videos. Our idea is based on leveraging cross-modal query and inter-frame dependencies to adaptively reduce temporal and spatial redundancy in videos. Specifically, we leverage DINOv2 features to remove redundant frames that exhibit high similarity. Then we utilize text-guided cross-modal query for selective frame feature reduction. Further, we perform spatial token reduction across frames based on their temporal dependencies. Our adaptive compression strategy effectively processes a large number of frames with little visual information loss within given context length. Our LongVU consistently surpass existing methods across a variety of video understanding benchmarks, especially on hour-long video understanding tasks such as VideoMME and MLVU. Given a light-weight LLM, our LongVU also scales effectively into a smaller size with state-of-the-art video understanding performance.

LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding

The paper "LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding" introduces an advanced methodology for processing long video content in the context of Multimodal LLMs (MLLMs). The primary challenge addressed is the limited context size of LLMs, which restricts their ability to effectively manage the information density of extended video scenes. This work proposes a novel framework, LongVU, which utilizes spatiotemporal adaptive compression to reduce video tokens while preserving crucial visual details.

Methodology Overview

LongVU addresses the issue of temporal and spatial redundancy in video content by employing a multi-step compression strategy. The proposed framework leverages cross-modal query techniques and inter-frame dependencies, thereby maximizing computational efficiency without significant loss of visual information.

1. Temporal Redundancy Reduction:
   • The methodology initiates with a temporal reduction strategy. This involves using DINOv2 features to discern and remove redundant frames based on similarity metrics, effectively condensing the sequence of frames.
2. Selective Feature Reduction:
   • Utilizes a text-guided cross-modal query to prioritize certain frames, preserving their full token capacity while reducing others through spatial pooling. This selective feature compression ensures that frames relevant to the query maintain their resolution, optimizing the alignment between visual and textual information.
3. Spatial Token Reduction:
   • Applies a spatial token reduction mechanism by analyzing temporal dependencies across frames. This step further refines the visual information, pruning tokens where redundancy is detectable.

Empirical Evaluation

LongVU demonstrates significant improvements over existing methods across multiple benchmarks, notably hour-long video understanding tasks such as VideoMME and MLVU. Empirical results show LongVU outperforming baseline models like LLaVA-OneVision by approximately 5% in average accuracy on video understanding benchmarks.

The research also includes rigorous evaluations comparing lightweight LongVU setups with smaller language backbones such as Llama3.2-3B, showing a performance increase of 3.4% on the VideoMME Long subset over previous state-of-the-art models. This highlights LongVU's scalability and efficiency, even when deployed in resource-constrained settings.

Implications and Future Directions

The proposed LongVU framework has significant implications for both theoretical and practical aspects of video LLMing. Theoretically, it advances the understanding of redundancy management in video processing, effectively bridging the gap between long video content and LLM limitations. Practically, it offers a scalable solution that can be optimized for lightweight applications, thereby broadening the usability of LLMs in real-world video processing tasks.

Future developments may explore integrating mixed media datasets, as current limitations restrict the model's optimization for both still images and video scenarios within resource constraints. Opportunities for improvement include enhancing the cross-modal query mechanisms and further refining the spatial token reduction techniques to increase efficacy in diverse application domains.

Conclusion

In conclusion, the LongVU framework marks a substantial step forward in the field of long video understanding using MLLMs. By effectively employing spatiotemporal adaptive compression, the methodology manages to harness rich video content without breaching the inherent limitations of LLM contexts. The work embodies a significant advancement in video-LLMing, offering a robust foundation for further exploration and enhancement in this burgeoning domain.