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
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 ...
Please like and share this post if you enjoyed it using the buttons at the bottom!
Stay up to date. Subscribe to my posts https://morrislee1234.wixsite.com/website/contact
Web site with my other posts by category https://morrislee1234.wixsite.com/website
Opmerkingen