22 citations · 60 across the 22 of their papers we have counts for
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CLFSeg: A Fuzzy-Logic based Solution for Boundary Clarity and Uncertainty Reduction in Medical Image Segmentation
Anshul Kaushal, Kunal Jangid, Vinod K. Kurmi
Accurate polyp and cardiac segmentation for early detection and treatment is essential for the diagnosis and treatment planning of cancer-like diseases. Traditional convolutional n…
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…
Multi Attribute Bias Mitigation via Representation Learning
Rajeev Ranjan Dwivedi, Ankur Kumar, Vinod K Kurmi
Real world images frequently exhibit multiple overlapping biases, including textures, watermarks, gendered makeup, scene object pairings, etc. These biases collectively impair the…
Strategic Base Representation Learning via Feature Augmentations for Few-Shot Class Incremental Learning
Parinita Nema, Vinod K Kurmi
Few-shot class incremental learning implies the model to learn new classes while retaining knowledge of previously learned classes with a small number of training instances. Existi…
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…
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…