Segment scene 2x faster using convolution, RWKV, and multiscale tokens with RWKV-SAM
Segment scene 2x faster using convolution, RWKV, and multiscale tokens with RWKV-SAM
Mamba or RWKV: Exploring High-Quality and High-Efficiency Segment Anything Model
arXiv paper abstract https://arxiv.org/abs/2406.19369
arXiv PDF paper https://arxiv.org/pdf/2406.19369
GitHub https://github.com/HarborYuan/ovsam
Transformer-based segmentation methods face the challenge of efficient inference when dealing with high-resolution images.
Recently, several linear attention architectures, such as Mamba and RWKV, have attracted much attention as they can process long sequences efficiently.
… design a mixed backbone that contains convolution and RWKV operation, which achieves the best for both accuracy and efficiency.
… design an efficient decoder to utilize the multiscale tokens to obtain high-quality masks.
… denote … method as RWKV-SAM, a simple, effective, fast baseline for SAM-like models.
… RWKV-SAM … more than 2x speedup and … better segmentation … outperforms recent vision Mamba … with better classification and semantic segmentation results …
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