S2S2 adds a pairwise feature-consistency loss between real and diffusion-generated images with the same segmentation map, improving Dice scores in most tested CT, MRI, and RGB medical segmentation benchmarks.
AI-SAM: Automatic and Interactive Segment Anything Model
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abstract
Semantic segmentation is a core task in computer vision. Existing methods are generally divided into two categories: automatic and interactive. Interactive approaches, exemplified by the Segment Anything Model (SAM), have shown promise as pre-trained models. However, current adaptation strategies for these models tend to lean towards either automatic or interactive approaches. Interactive methods depend on prompts user input to operate, while automatic ones bypass the interactive promptability entirely. Addressing these limitations, we introduce a novel paradigm and its first model: the Automatic and Interactive Segment Anything Model (AI-SAM). In this paradigm, we conduct a comprehensive analysis of prompt quality and introduce the pioneering Automatic and Interactive Prompter (AI-Prompter) that automatically generates initial point prompts while accepting additional user inputs. Our experimental results demonstrate AI-SAM's effectiveness in the automatic setting, achieving state-of-the-art performance. Significantly, it offers the flexibility to incorporate additional user prompts, thereby further enhancing its performance. The project page is available at https://github.com/ymp5078/AI-SAM.
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S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging
S2S2 adds a pairwise feature-consistency loss between real and diffusion-generated images with the same segmentation map, improving Dice scores in most tested CT, MRI, and RGB medical segmentation benchmarks.