Introducing Segment Anything: Working toward the first foundation model for image segmentation

Zero-shot image segmentation with CLIPSeg

Universal Image Segmentation with Mask2Former and OneFormer

Don't waste SAM

Meta AI has recently released the Segment Anything Model (SAM), which demonstrates exceptional zero-shot image segmentation performance across various tasks with remarkable accuracy. Despite its inability to provide accurate segmentation across multiple research fields, SAM still serves as a valuable starting point for supporting the segmentation pipeline process, particularly for tasks that require extensive and senior skills annotations.

Glass Segmentation with Fusion of Learned and General Visual Features

The paper introduces a dual‑backbone architecture for glass segmentation that combines a frozen foundation model with a learned backbone trained on glass‑specific data. By fusing hierarchical multi‑scale features from both backbones, the method produces accurate segmentation masks and achieves state‑of‑the‑art performance on four benchmark datasets. Ablation studies confirm the benefits of the dual‑backbone design and its generalizability across different backbone choices, while also offering competitive inference speeds, especially with lighter backbones.

Mask Proposal Voting Based on Geodesic Framework for Robust Image Segmentation

arXiv:2606. 14912v1 Announce Type: cross Abstract: Despite great advances, finding accurate segmentation remains a challenging task, especially in scenarios with cluttered backgrounds, complex intensity variations and topology appearance.

Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation

arXiv:2606. 04705v1 Announce Type: cross Abstract: Semantic segmentation in medical imaging is a critical yet challenging task due to data scarcity and high variability across modalities.

Fine-Tune a Semantic Segmentation Model with a Custom Dataset

A Comprehensive Survey of Medical Image Segmentation: Challenges, Benchmarks, and Beyond

arXiv:2606. 16153v1 Announce Type: cross Abstract: Medical image segmentation plays a critical role in clinical diagnostics, treatment planning, disease monitoring, and neurological disorder identification.

Domain-Guided Prompting of the Segment Anything Model for Seismic Interpretation: The Role of Attributes, Visualization, and Hybrid Prompts

arXiv:2606. 15786v1 Announce Type: cross Abstract: The advent of large pretrained foundation models for computer vision has significantly improved the efficiency of visual data interpretation.

Conformal Prediction Sets for Instance Segmentation

arXiv:2602. 10045v2 Announce Type: replace-cross Abstract: Current instance segmentation models achieve high performance on average predictions, but lack principled uncertainty quantification: their outputs are not calibrated, and there is no guarantee that a predicted mask is close to the ground truth.

Seeing as Humans Do: Learning from Motion to Segment Anything Without Supervision

arXiv:2609.39785v1 Announce Type: new Abstract: The Segment Anything Model (SAM) relies heavily on massive manual annotations, creating a fundamental bottleneck for model scaling. While unsupervised...

Combining Foundation Model Confidence and Monocular Depth for Training-Free Out-of-Distribution Segmentation

arXiv:2609.22896v1 Announce Type: new Abstract: Autonomous vehicles operating in open-world scenarios are inevitably confronted with previously unknown objects, such as exotic animals or loose cargo....