Fine-Tune a Semantic Segmentation Model with a Custom Dataset

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.

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

The Impact of CutMix on Reliability and Robustness in Semantic Segmentation

The paper investigates how the CutMix data augmentation technique affects reliability and robustness in semantic segmentation. It evaluates two architectures—CNN-based DeepLabV3+ and transformer-based SegFormer—on both in-domain and out-of-domain data. Results show that CutMix has a minor effect on segmentation accuracy but consistently improves reliability, especially under distribution shifts, by enhancing calibration and uncertainty quality.

Weakly Supervised Segmentation as Semantic-Based Regularization

arXiv:2605. 13674v2 Announce Type: replace-cross Abstract: Weakly supervised semantic segmentation (WSSS) trains dense pixel-level segmentation models from partial or coarse annotations such as bounding boxes, scribbles, or image-level tags.

Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models

arXiv:2606. 31603v1 Announce Type: cross Abstract: Semantic segmentation models struggle with data sparsity and rare or visually diverse regions, e.

Sub-Semantic Image Segmentation

arXiv:2606. 14754v1 Announce Type: cross Abstract: Images can be segmented based on visual cues (i.