The state space model (SSM) is gaining interest due to advancements, benefiting from concurrent training to capture long-range dependencies. Vision Mamba (Vim) aims to overcome obstacles in visual backbone design. It combines position embeddings and bidirectional SSMs for global context modeling. Vim shows promise for image modeling and dense prediction with efficient computation. For more details, see the Paper and Github. (50 words)
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State Space Model (SSM) Advancements in AI
Modern SSMs for Long-Range Dependency Modeling
Recent advancements in state space models (SSMs) have led to the development of efficient methods like linear state-space layers (LSSL), structured state-space sequence model (S4), diagonal state space (DSS), and S4D. These methods excel at capturing long-range dependencies and are efficient on lengthy sequences.
Vision Mamba (Vim) for Visual Backbone
The Vision Mamba (Vim) block, developed by researchers, overcomes obstacles in vision modeling by combining position embeddings for location-aware visual identification with bidirectional SSMs for data-dependent global visual context modeling. Vim is particularly reliable for dense prediction tasks and can be pretrained on massive amounts of unsupervised visual input, improving its visual representation.
Efficiency and Performance of Vim
Vim, a pure-SSM-based approach, shows promise as a general and efficient backbone for vision applications. It achieves the same modeling power as ViT without requiring attention, saving GPU RAM and offering faster performance. Vim outperforms other models in high-resolution computer vision applications like video segmentation, computational pathology, medical picture segmentation, and aerial image analysis.
Future Applications and Integration
The bidirectional SSM modeling with position embeddings in Vim opens up opportunities for tackling unsupervised tasks and multimodal applications. Pretrained Vim weights can be used for downstream tasks involving long films, high-resolution medical images, and remote sensing photos.
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