Fine-Tune a Semantic Segmentation Model with a Custom Dataset

Segmentation of Bovid Dentition Under Imperfect Annotations: A Comparative Study of Convolutional and Attention Models

arXiv:2608.31052v1 Announce Type: cross Abstract: Semantic segmentation decomposes an image into distinct mask regions corresponding to different object categories, such as people, cars, signs or bui...

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.

A Framework for Low-Effort Training Data Generation for Urban Semantic Segmentation

The paper introduces a framework that adapts a diffusion model to a target urban domain using only imperfect pseudo‑labels, enabling the generation of high‑fidelity, target‑aligned images from semantic maps of any synthetic dataset. By filtering poor generations, correcting image‑label misalignments, and standardising semantics, the method transforms low‑effort synthetic data into competitive real‑domain training sets. Experiments on five synthetic and two real datasets show up to +8.0 %pt mIoU improvement over state‑of‑the‑art translation methods, demonstrating that rapidly constructed synthetic datasets can match the performance of high‑effort, manually designed ones.