TinySAM 2: Extreme Memory Compression for Efficient Track Anything Model
Researchers have developed TinySAM 2, a more efficient version of the Segment Anything Model 2 (SAM 2) for video segmentation and object tracking. TinySAM 2 employs a memory quality management mechanism and joint spatial-temporal token compression to significantly reduce memory storage and computational costs. This optimization allows the model to achieve 90% of SAM 2.1's performance using only 7% of the memory tokens and 3% of the training data, making it more suitable for deployment on resource-constrained devices. AI
IMPACT Enables wider deployment of advanced video segmentation models on devices with limited computational resources.