Semantic Segmentation
Overview
Semantic segmentation predicts a category for each pixel, supporting applications such as autonomous driving, medical imaging, and remote sensing. Common approaches include CNN and vision-transformer encoders with multi-scale decoders, while foundation models, vision-language prompts, and diffusion features are increasingly adapted for dense prediction. To reduce annotation demands, researchers use weak or sparse labels, pseudo-labeling, synthetic data, few-shot learning, and domain adaptation; RGB-D, thermal, radar, and other sensors provide complementary cues. Key challenges are reliable transfer across domains and conditions, accurate boundaries and rare classes, and balancing accuracy with calibration and inference cost.
Latest papers
416 papers33 papers in the last four weeks, down 20% on the four weeks before. 0.2% of all new papers.