A framework uses modality-agnostic prompts to adapt SAM for multi-modal camouflaged object detection, with a mask refine module for better boundaries.
SAM2-Adapter: Evaluating & adapting Seg- ment Anything 2 in downstream tasks: Camouflage, shadow, medical image segmentation, and more
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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.
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 foundation models.
citing papers explorer
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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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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 foundation models.