Look Every Frame All at Once: Video-Ma$^2$mba for Efficient Long-form Video Understanding with Multi-Axis Gradient Checkpointing (2411.19460v1)

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$^2$mba, 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$^2$mba 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.

• The paper introduces Video-Ma2mba, which replaces transformers with SSM-based Mamba-2 to achieve linear memory scaling.
• It employs Multi-Axis Gradient Checkpointing to significantly reduce memory overhead during long video sequence processing.
• Empirical results demonstrate the model's capability to handle hours-long videos on a single GPU, improving efficiency in video understanding.

Overview of "Look Every Frame All at Once: Video-Ma$^2$mba for Efficient Long-form Video Understanding with Multi-Axis Gradient Checkpointing"

The presented paper addresses the challenges associated with processing long video sequences using transformer-based Large Multi-modal Models (LMMs), which traditionally suffer from quadratic scaling of memory and computational needs. This is particularly problematic as video data continues to expand in both complexity and duration. To tackle this, the authors propose a novel architecture, Video-Ma$^2$mba, which leverages State Space Models (SSMs) under the Mamba-2 framework, effectively substituting attention mechanisms to attain linear scalability concerning time and memory.

Key Contributions

1. Introducing Video-Ma$^2$mba: The architecture replaces transformers with Mamba-2, employing SSMs to improve memory efficiency. This structural change results in linear scaling of computational requirements, making it feasible to handle extraordinarily long video sequences.
2. Multi-Axis Gradient Checkpointing (MA-GC): MA-GC is a pivotal innovation presented in this work. By implementing gradient checkpointing across multiple computational axes, the method significantly reduces memory overheads typical with standard checkpointing techniques. This strategy enhances the model's ability to process sequences spanning millions of tokens on a single GPU.
3. Efficiency and Practical Implications: Empirical results highlight the capacity of Video-Ma$^2$mba to handle extensive, continuous video inputs efficiently, greatly surpassing existing frameworks in terms of maintaining temporal dynamics and producing accurate responses.

Empirical Findings

The authors provide strong numerical results through their experimental setups, demonstrating how Video-Ma$^2$mba significantly trims down memory usage compared to traditional attention-based frameworks. Specifically, processing equivalent to over two hours of video sequences at 1 FPS on a single GPU underscores the model's capability. This denotes a substantial step forward in terms of computational efficiency for video understanding tasks within multi-modal models.

Implications and Future Directions

The practicality of Video-Ma$^2$mba in handling long video sequences opens up exciting possibilities for real-world applications across various fields, such as surveillance, entertainment, and autonomous systems, where managing extensive video data is paramount.

Theoretically, the insights gained from integrating SSMs within the Mamba-2 framework could inform future architectures that seek to optimize the trade-off between memory consumption and computational efficiency. Additionally, the MA-GC technique provides a framework that other large-scale sequence processing tasks may adopt.

Looking ahead, further research could explore scaling the model to accommodate even longer sequences or deploying it in more diverse multi-modal contexts. Investigations into improving the efficiency of SSMs or exploring hybrid models that combine these techniques with transformer architectures could further enhance the landscape of long-sequence processing in AI.

In summary, the advancements proposed by Video-Ma$^2$mba introduce a scalable and efficient approach to video understanding, promising impactful contributions across both academic research and practical applications in AI and beyond.