3 citations · 6 across the 12 of their papers we have counts for
5 papers · 1 filter
GenDeg: Diffusion-based Degradation Synthesis for Generalizable All-In-One Image Restoration
Sudarshan Rajagopalan, Nithin Gopalakrishnan Nair, Jay N. Paranjape +1
Deep learning-based models for All-In-One Image Restoration (AIOR) have achieved significant advancements in recent years. However, their practical applicability is limited by poor…
Federated Black-Box Adaptation for Semantic Segmentation
Jay N. Paranjape, Shameema Sikder, S. Swaroop Vedula +1
Federated Learning (FL) is a form of distributed learning that allows multiple institutions or clients to collaboratively learn a global model to solve a task. This allows the mode…
S-SAM: SVD-based Fine-Tuning of Segment Anything Model for Medical Image Segmentation
Jay N. Paranjape, Shameema Sikder, S. Swaroop Vedula +1
Medical image segmentation has been traditionally approached by training or fine-tuning the entire model to cater to any new modality or dataset. However, this approach often requi…
A Mamba-based Siamese Network for Remote Sensing Change Detection
Jay N. Paranjape, Celso de Melo, Vishal M. Patel
Change detection in remote sensing images is an essential tool for analyzing a region at different times. It finds varied applications in monitoring environmental changes, man-made…
Blackbox Adaptation for Medical Image Segmentation
Jay N. Paranjape, Shameema Sikder, S. Swaroop Vedula +1
In recent years, various large foundation models have been proposed for image segmentation. There models are often trained on large amounts of data corresponding to general compute…