Researchers have developed a new machine learning model called VideoMamba, which employs a state space model (SSM) for efficient video understanding. VideoMamba utilizes 3D patch embeddings passed into bidirectional Mamba blocks for video classification, offering superior scaling capabilities compared to existing architectures like TimeSformer. This advancement has been made possible through collaborative efforts, including significant contributions from Tel Aviv University, where researchers have proposed reformulating Mamba computation to bridge gaps in understanding through self-attention layers. VideoMamba overcomes the limitations of existing 3D convolution neural networks and video transformers by introducing a linear-complexity operator that enables efficient long-term modeling, crucial for high-resolution video analysis. Furthermore, VideoMamba has demonstrated high sensitivity to quick, subtle movements and superiority in long-term video understanding with minimal pre-training required, and it is available with open access on the Hub.
VideoMamba🐍 A purely SSM-based model for video understanding with open access on the Hub🔥 Model: https://t.co/3EohrweA5F ✨ Scales well visually with little pre-training ✨ Highly sensitive to quick, subtle movements ✨ Superiority in long-term video understanding ✨…
Motion Mamba Efficient and Long Sequence Motion Generation with Hierarchical and Bidirectional Selective SSM Human motion generation stands as a significant pursuit in generative computer vision, while achieving long-sequence and efficient motion generation remains https://t.co/Lo4HN1goNu
VideoMamba. This work 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 crucial for high-resolution long… https://t.co/tMjjsZkKnh
This Machine Learning Research from Tel Aviv University Reveals a Significant Link between Mamba and Self-Attention Layers Quick read: https://t.co/nasWv5C9ZI Tel Aviv University researchers have proposed reformulating Mamba computation to address gaps in understanding using a…
VideoMamba: State Space Model for Efficient Video Understanding abs: https://t.co/3ZxSRdfSBw code: https://t.co/IOHTKsp4Rm VideoMamba uses 3D patch embeddings passed into bidirectional Mamba blocks for video classification. Scales better than existing archs like TimeSformer for… https://t.co/wRsWZABf29