VideoMamba: State Space Model for Efficient Video Understanding

Conceptos Básicos

The author introduces VideoMamba, a state space model tailored for video understanding, highlighting its scalability and efficiency in processing long videos.

Resumen

VideoMamba is a novel approach that addresses the challenges of video understanding by leveraging state space models. It offers superior performance in recognizing short-term actions and interpreting long videos efficiently. The model sets a new benchmark for comprehensive video understanding with its unique capabilities.

Estadísticas

VideoMamba operates 6× faster than TimeSformer [4] and demands 40× less GPU memory for 64-frame videos.
VideoMamba-M achieves similar performances to ViViT-L [2] but with fewer parameters.
Increasing the number of frames consistently enhances results on the Kinetics-400 dataset.
VideoMamba-Ti shows a notable increase of +6.1% over ViS4mer using Swin-B features.

Citas

"Through these distinct advantages, VideoMamba sets a new benchmark for video understanding."
"VideoMamba harmoniously merges the strengths of convolution and attention in vanilla ViT style."

Ideas clave extraídas de

VideoMamba

by Kunchang Li,... a las arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06977.pdf

Consultas más profundas

How does VideoMamba compare to other state-of-the-art models in terms of computational efficiency

VideoMamba stands out in terms of computational efficiency compared to other state-of-the-art models due to its linear-complexity operator. This unique feature enables VideoMamba to efficiently handle long sequences and high-resolution videos without the need for extensive computational resources. In comparison, traditional attention-based models often come with higher computational costs for processing long sequences, making VideoMamba a more efficient solution for video understanding tasks.

What are the potential implications of VideoMamba's linear-complexity operator for future video processing tasks

The linear-complexity operator of VideoMamba has significant implications for future video processing tasks. By offering a method that can efficiently model dynamic spatiotemporal contexts in high-resolution long videos, VideoMamba opens up possibilities for handling complex video data with reduced computational overhead. This could lead to advancements in various applications such as action recognition, activity detection, and video captioning by enabling faster processing speeds and lower GPU memory requirements.

How might the integration of multi-modal datasets impact the performance of VideoMamba

The integration of multi-modal datasets can have a positive impact on the performance of VideoMamba by enhancing its robustness and versatility in comprehending different types of data inputs. By pretraining on joint vision-text datasets like WebVid-2M and CC3M, VideoMamba can improve its ability to understand both visual and textual information simultaneously. This integration allows VideoMamba to excel in tasks requiring multi-modal comprehension, such as video-text retrieval, question answering, and cross-modal alignment. Ultimately, leveraging multi-modal datasets can enhance the overall performance and adaptability of VideoMamba across diverse video understanding scenarios.