Summary of Lite-sam Is Actually What You Need For Segment Everything, by Jianhai Fu et al.
Lite-SAM Is Actually What You Need for Segment Everything
by Jianhai Fu, Yuanjie Yu, Ningchuan Li, Yi Zhang, Qichao Chen, Jianping Xiong, Jun Yin, Zhiyu Xiang
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Lite-SAM, an efficient end-to-end solution for the SegEvery task designed to reduce computational costs and redundancy. It consists of four main components: a streamlined CNN-Transformer hybrid encoder (LiteViT), an automated prompt proposal network (AutoPPN), a traditional prompt encoder, and a mask decoder. The LiteViT is a high-performance lightweight backbone network with only 1.16M parameters, a 23% reduction compared to the lightest existing backbone network Shufflenet. The paper also introduces AutoPPN, an innovative end-to-end method for prompt boxes and points generation, which improves over traditional grid search sampling methods. Lite-SAM is integrated within the SAM framework and has been thoroughly benchmarked across various public and private datasets using universal metrics such as number of parameters, execution time, and accuracy. The findings reveal that Lite-SAM outperforms its counterparts, achieving performance improvements of 43x, 31x, 20x, 21x, and 1.6x over SAM, MobileSAM, Edge-SAM, EfficientViT-SAM, and MobileSAM-v2 respectively, while maintaining competitive accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Lite-SAM is a new solution for the SegEvery task that makes computers work more efficiently. It has four main parts: an encoder (LiteViT), a way to generate prompts (AutoPPN), another prompt encoder, and a mask decoder. Lite-ViT is a special kind of computer network with only 1.16 million parameters, which is less than other networks like Shufflenet. The paper also introduces AutoPPN, a new way to generate prompts that’s better than traditional methods. This solution has been tested on many datasets and shows great results. |
Keywords
» Artificial intelligence » Cnn » Decoder » Encoder » Grid search » Mask » Prompt » Sam » Transformer » Vit
