Deformba: Vision State Space Model with Adaptive State Fusion
Researchers have introduced Deformba, a novel context-adaptive method designed to enhance the application of State Space Models (SSMs) to vision tasks. Deformba addresses limitations in existing vision SSMs by dynamically augmenting spatial structural information while preserving linear complexity, and it enables multi-modal fusion capabilities like cross-attention. The method has demonstrated strong performance across various 2D vision tasks, including image classification, object detection, and segmentation, as well as 3D vision tasks such as BEV perception. AI
IMPACT Introduces a new method to improve the efficiency and applicability of State Space Models in computer vision tasks.