3 papers
cs.CV2025
Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment
Rini Smita Thakur, Rajeev Ranjan Dwivedi, Vinod K Kurmi
Accurate segmentation of the optic disc and cup is critical for the early diagnosis and management of ocular diseases such as glaucoma. However, segmentation models trained on one…
cs.CV2025
Label Calibration in Source Free Domain Adaptation
Shivangi Rai, Rini Smita Thakur, Kunal Jangid +1
Source-free domain adaptation (SFDA) utilizes a pre-trained source model with unlabeled target data. Self-supervised SFDA techniques generate pseudolabels from the pre-trained sour…
cs.CV2025
Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation
Rini Smita Thakur, Vinod K. Kurmi
Semi-supervised (SS) semantic segmentation exploits both labeled and unlabeled images to overcome tedious and costly pixel-level annotation problems. Pseudolabel supervision is one…