SCALER: SAM-Enhanced Collaborative Learning for Label-Deficient Concealed Object Segmentation

MedSAM-3 has been introduced as a text promptable medical segmentation model designed to enhance medical image and video segmentation by allowing precise targeting of anatomical structures through open-vocabulary text descriptions. This model builds on the Segment Anything Model (SAM) 3 architecture, addressing the limitations of existing methods that require extensive manual annotation for clinical applications.

The introduction of DEAP-3DSAM, or Decoder Enhanced and Auto Prompt SAM, marks a significant advancement in 3D medical image segmentation, building on the capabilities of the Segment Anything Model (SAM). This new model addresses limitations in spatial feature retention and the reliance on manual prompts, which have hindered previous attempts at applying SAM to 3D images.

SGDFuse has been introduced as a conditional diffusion model that leverages the Segment Anything Model (SAM) to enhance infrared and visible image fusion, addressing challenges such as detail loss and artifacts in existing methods. This two-stage process utilizes high-quality semantic masks to guide the optimization of the fusion process, aiming for high-fidelity and semantically-aware results.

A new framework called Attention-Guided Efficient Vision-Language Models (AGE-VLM) has been introduced to enhance the alignment between visual and textual information in Large Vision-Language Models (VLMs). This approach utilizes interleaved cross-attention layers and spatial knowledge from the Segment Anything Model (SAM) to improve visual grounding and reduce hallucinations in image-text pairings.

The Segment Anything Model (SAM) 3 has been introduced as a unified framework capable of detecting, segmenting, and tracking objects in images and videos using concept prompts. This model enhances the Promptable Concept Segmentation (PCS) by utilizing a scalable data engine that generates a dataset with 4 million unique concept labels, significantly improving segmentation accuracy in both images and videos.

A new study introduces Continual Alignment for SAM (CA-SAM), a strategy aimed at enhancing the Segment Anything Model (SAM) for medical image segmentation. This approach addresses the challenges of heterogeneous privacy policies across institutions that hinder joint training on pooled datasets, allowing for continual learning from data streams without catastrophic forgetting.