VideoMamba: State Space Model for Efficient Video Understanding (2403.06977v2)

Abstract: Addressing the dual challenges of local redundancy and global dependencies in video understanding, this work innovatively adapts the Mamba to the video domain. The proposed VideoMamba overcomes the limitations of existing 3D convolution neural networks and video transformers. Its linear-complexity operator enables efficient long-term modeling, which is crucial for high-resolution long video understanding. Extensive evaluations reveal VideoMamba's four core abilities: (1) Scalability in the visual domain without extensive dataset pretraining, thanks to a novel self-distillation technique; (2) Sensitivity for recognizing short-term actions even with fine-grained motion differences; (3) Superiority in long-term video understanding, showcasing significant advancements over traditional feature-based models; and (4) Compatibility with other modalities, demonstrating robustness in multi-modal contexts. Through these distinct advantages, VideoMamba sets a new benchmark for video understanding, offering a scalable and efficient solution for comprehensive video understanding. All the code and models are available at https://github.com/OpenGVLab/VideoMamba.

VideoMamba: An Efficient SSM-based Model for Video Understanding

Introduction

Recent developments in video understanding have spotlighted the significance of mastering spatiotemporal representations to tackle the inherent challenges of large spatiotemporal redundancy within short video clips and complex spatiotemporal dependencies in long video segments. VideoMamba represents an innovative adaptation of the Mamba model to the video domain, aiming to address these challenges effectively. This work proposes a scalable and efficient solution, termed VideoMamba, which shows remarkable capabilities in handling both short-term and long-term video understanding without the need for extensive dataset pretraining.

Core Contributions

The introduction of VideoMamba is a step forward in video understanding research, setting new benchmarks through its distinct capabilities:

• Scalability in the Visual Domain: VideoMamba demonstrates exceptional scalability, which is attributed to a novel self-distillation technique. This technique allows the model to enhance its performance significantly as it scales, which is critical for high-resolution and long-duration video understanding.
• Sensitivity for Short-term Action Recognition: The model shows an enhanced sensitivity to recognizing short-term actions, especially those involving fine-grained motion distinctions. This advancement is notable over traditional attention-based models, making VideoMamba a superior choice for tasks requiring nuanced understanding of video content.
• Superiority in Long-term Video Understanding: VideoMamba excels in interpreting long videos through end-to-end training, indicating a substantial improvement over conventional feature-based methods. Its ability to operate significantly faster while consuming markedly less GPU memory highlights its efficiency and effectiveness.
• Compatibility with Other Modalities: The model's robustness in multi-modal contexts is evidenced by its improved performance in video-text retrievals, particularly for long videos with complex scenarios. This compatibility underscores VideoMamba's potential in applications requiring robust multi-modal integration.

Technical Method

At the heart of VideoMamba's architecture is the selective state space model (SSM), which harmoniously blends the strengths of convolution and attention mechanisms. This design choice facilitates linear-complexity dynamic spatiotemporal context modeling, optimal for high-resolution and extended video analysis. A key innovation is the model's spatial-first bidirectional scan, which proves to be both effective and efficient. Additionally, VideoMamba's design incorporates a simplified structure and an effective self-distillation strategy to counteract potential overfitting issues, ensuring scalable performance across different tasks.

Future Directions and Limitations

While VideoMamba sets a new standard in video understanding, future explorations could extend to larger model sizes, incorporate additional modalities such as audio, and explore integration with LLMs for more comprehensive video comprehension tasks. Moreover, further research could assess the model’s scalability and its application in real-world scenarios, which range from content recommendation systems to autonomous vehicle navigation.

Conclusion

VideoMamba introduces a novel approach to video understanding, leveraging the efficiency of SSMs to deliver a model that is not only scalable but also capable of excelling in both short-term and long-term video analysis tasks. Its compatibility with other modalities opens new avenues for research in multi-modal video understanding. With its source code and model made openly available, VideoMamba invites further exploration and advancement in the field, paving the way for more sophisticated and efficient video understanding solutions.