EfficientViT-SAM: New Advancements in Image Segmentation
The introduction of the Segment Anything Model (SAM) has revolutionized image segmentation. However, its high computational intensity has limited its application in time-sensitive scenarios.
Efforts to address this challenge led to the development of models like MobileSAM, EdgeSAM, and EfficientSAM. However, while they reduce computational costs, they experience drops in performance.
To enhance SAM’s efficiency without compromising accuracy, EfficientViT-SAM was introduced. It utilizes the EfficientViT architecture to revamp SAM’s image encoder, resulting in two variants: EfficientViT-SAM-L and EfficientViT-SAM-XL.
EfficientViT stands at the core of this innovation, offering a unique multi-scale linear attention module that significantly reduces computational complexity without compromising the model’s ability to perceive and learn multi-scale features.
EfficientViT-SAM’s architecture is structured into five stages, efficiently fusing multi-scale features to enhance the model’s segmentation capability.
The model’s excellence is proven with empirical performance, demonstrating superior segmentation accuracy and efficiency.
EfficientViT-SAM models have been made open-source, enabling further research and development of image segmentation models.
For the full research paper, visit https://arxiv.org/abs/2402.05008. For more updates and information regarding the research, follow them on Twitter and Google News, or join their growing community on Reddit, Facebook, Discord, and LinkedIn.
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