Look Every Frame All at Once: Video-Ma$^2$mba for Efficient Long-form Video Understanding with Multi-Axis Gradient Checkpointing
Hosu Lee, Junho Kim, Hyunjun Kim, Yong Man Ro
2024-12-02
Summary
This paper presents Video-Ma²mba, a new method designed to improve how we understand long videos by making the processing faster and more efficient.
What's the problem?
As videos get longer and more complex, traditional methods for analyzing them can struggle because they require a lot of memory and computing power. This makes it difficult to process long video sequences effectively, leading to slow performance and potential inaccuracies in understanding the content.
What's the solution?
Video-Ma²mba tackles this issue by using a combination of State Space Models (SSMs) and a technique called Multi-Axis Gradient Checkpointing (MA-GC). SSMs help manage the way information flows through the video data, allowing for linear scaling in memory and processing time. The MA-GC method optimizes memory usage by only keeping track of essential data during computations, which significantly reduces the amount of memory needed. Together, these innovations allow the model to handle very long videos—up to two hours—while maintaining high accuracy in understanding the content.
Why it matters?
This research is important because it enables better analysis of long videos, which is crucial for various applications like video editing, surveillance, and content creation. By improving how we process and understand video data, Video-Ma²mba can enhance our ability to extract meaningful insights from extensive visual information, making it a valuable tool in fields that rely on video analysis.
Abstract
With the growing scale and complexity of video data, efficiently processing long video sequences poses significant challenges due to the quadratic increase in memory and computational demands associated with existing transformer-based Large Multi-modal Models (LMMs). To address these issues, we introduce Video-Ma^2mba, a novel architecture that incorporates State Space Models (SSMs) within the Mamba-2 framework, replacing the attention mechanisms. This allows the LMMs to scale linearly in terms of time and memory requirements, making it feasible to handle long-duration video content. Furthermore, we enhance the memory efficiency introducing the Multi-Axis Gradient Checkpointing (MA-GC) method, which strategically manages memory by retaining only essential activations across multiple computational axes. Our approach significantly reduces the memory footprint compared to standard gradient checkpointing. Empirical analyses show that Video-Ma^2mba can process extensive video sequences-equivalent to millions of tokens or over two hours of continuous sequences at 1 FPS-on a single GPU. By maintaining a detailed capture of temporal dynamics, our model improves the accuracy and relevance of responses in long video understanding tasks, demonstrating substantial advantages over existing frameworks.