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SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More
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SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More
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The advent of large models, also known as foundation models, has significantly transformed the AI research landscape, with models like Segment Anything (SAM) achieving notable success in diverse image segmentation scenarios. Despite its advancements, SAM encountered limitations in handling some complex low-level segmentation tasks like camouflaged object and medical imaging. In response, in 2023, we introduced SAM-Adapter, which demonstrated improved performance on these challenging tasks. Now, with the release of Segment Anything 2 (SAM2), a successor with enhanced architecture and a larger training corpus, we reassess these challenges. This paper introduces SAM2-Adapter, the first adapter designed to overcome the persistent limitations observed in SAM2 and achieve new state-of-the-art (SOTA) results in specific downstream tasks including medical image segmentation, camouflaged (concealed) object detection, and shadow detection. SAM2-Adapter builds on the SAM-Adapter's strengths, offering enhanced generalizability and composability for diverse applications. We present extensive experimental results demonstrating SAM2-Adapter's effectiveness. We show the potential and encourage the research community to leverage the SAM2 model with our SAM2-Adapter for achieving superior segmentation outcomes. Code, pre-trained models, and data processing protocols are available at http://tianrun-chen.github.io/SAM-Adaptor/
Forward citations
Cited by 9 Pith papers
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Modality-Agnostic Prompt Learning for Multi-Modal Camouflaged Object Detection
A framework uses modality-agnostic prompts to adapt SAM for multi-modal camouflaged object detection, with a mask refine module for better boundaries.
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Global attention over a high-resolution token set, not capacity or pretraining, determines whether LoRA adapters convert resolution into accuracy on fine-grained segmentation.
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Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion
A curriculum-then-anti-curriculum training schedule, ending with spectral low-pass fine-tuning, improves context-entangled segmentation across several datasets and backbones.
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From Reconstruction to Decision: A Post-Encoder Plug-in Adapter for Curvilinear Segmentation
PEPA is a post-encoder adapter combining target-conditioned snake upsampling and adaptive differentiable thresholding that improves topological metrics over region overlap when added to frozen-encoder curvilinear segm...
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M$^4$-SAM: Multi-Modal Mixture-of-Experts with Memory-Augmented SAM for RGB-D Video Salient Object Detection
M⁴-SAM equips SAM2 with modality-aware MoE-LoRA, gated multi-level fusion, and pseudo-guided initialization to reach state-of-the-art on RGB-D video salient object detection.
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Weight Group-wise Post-Training Quantization for Medical Foundation Model
Permutation-COMQ is a new post-training quantization algorithm that reorders weights within layers and uses only dot-product and rounding steps to deliver the highest reported accuracy for 2-, 4-, and 8-bit medical fo...
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Multimodal SAM-adapter for Semantic Segmentation
A side-tuning adapter injects RGB-plus-auxiliary-sensor fused features into SAM's encoder, reaching state-of-the-art semantic segmentation on DeLiVER, FMB, and MUSES.
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DifferSeg: Towards Diverse Multimodal Binary Segmentation via Differential Perception and Frequency Guidance
DifferSeg introduces learnable differential operators for modality fusion and cross-frequency decoder interactions, claiming superior performance over 67 prior methods on 29 datasets across 18 tasks.
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BED-SAM2: Boundary-Enhanced-Depth SAM2 via Monocular Geometric Priors
BED-SAM2 enhances the SAM2 vision model by integrating monocular geometric priors to improve boundary delineation in object segmentation tasks.
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