Summary of Hrsam: Efficient Interactive Segmentation in High-resolution Images, by You Huang et al.
HRSAM: Efficient Interactive Segmentation in High-Resolution Images
by You Huang, Wenbin Lai, Jiayi Ji, Liujuan Cao, Shengchuan Zhang, Rongrong Ji
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The Segment Anything Model (SAM) has made significant advancements in interactive segmentation but is hindered by its high computational cost on high-resolution images, requiring downsampling and sacrificing fine-grained details. To overcome this limitation, the proposed HRSAM model leverages visual length extrapolation to generalize from low resolutions to high resolutions. The study begins by exploring the link between extrapolation and attention scores, leading to a Swin attention-based architecture. A Flexible Local Attention (FLA) framework is introduced, utilizing CUDA-optimized Efficient Memory Attention for acceleration, along with Flash Swin attention achieving a 35% speedup over traditional Swin attention. Additionally, the Cycle-scan module uses State Space models to efficiently expand HRSAM’s receptive field. Furthermore, the HRSAM++ model within FLA adds an anchor map, providing multi-scale data augmentation and a larger receptive field at a slight computational cost. Experimental results demonstrate that standard-trained HRSAMs surpass the previous state-of-the-art (SOTA) with 38% of the latency, while SAM-distillation enables HRSAMs to outperform teacher models at lower latency. Finetuning achieves performance significantly exceeding the previous SOTA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Segment Anything Model (SAM) is a powerful tool for interactive segmentation, but it has limitations when working with high-resolution images. To fix this issue, researchers developed a new model called HRSAM that can work on both low and high resolution images. HRSAM uses something called visual length extrapolation to make predictions about the image. The study also introduced a special type of attention called Swin attention that helps the model focus on important parts of the image. To make the process faster, they created a framework called FLA that uses a special type of memory optimization. The results show that HRSAM can work much faster than SAM and can even perform better when fine-tuned. |
Keywords
» Artificial intelligence » Attention » Data augmentation » Distillation » Optimization » Sam
