Universal Image Segmentation with Mask2Former and OneFormer

Zero-shot image segmentation with CLIPSeg

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

LUMA: Benchmarking Segmentation via a Lightweight Universal Mask Adapter

arXiv:2607. 00687v1 Announce Type: cross Abstract: Comparing transformer backbones for image segmentation is confounded: each is paired with a different decoder, recipe, and pretraining, so reported differences rarely reflect the backbone itself.

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.

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.

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.