3 citations · 6 across the 12 of their papers we have counts for
14 papers
Grounded Human-Attributed Description and Activity Recognition in Videos
Jay N. Paranjape, Jurijs Nazarovs, Rohan Sarkar +5
Understanding characteristics and activities of individuals in complex multi-person environments is crucial for real-world applications. However, existing datasets often simplify t…
Thermo-VL: Extending Vision-Language Models to Thermal Infrared Perception
Rusiru Thushara, Yasiru Ranasinghe, Jay Paranjape +1
Vision-language models (VLMs) often fail under low illumination because their visual grounding is learned predominantly from RGB imagery, whereas thermal infrared preserves complem…
Referring Change Detection in Remote Sensing Imagery
Yilmaz Korkmaz, Jay N. Paranjape, Celso M. de Melo +1
Change detection in remote sensing imagery is essential for applications such as urban planning, environmental monitoring, and disaster management. Traditional change detection met…
F-ViTA: Foundation Model Guided Visible to Thermal Translation
Jay N. Paranjape, Celso de Melo, Vishal M. Patel
Thermal imaging is crucial for scene understanding, particularly in low-light and nighttime conditions. However, collecting large thermal datasets is costly and labor-intensive due…
RP-SAM2: Refining Point Prompts for Stable Surgical Instrument Segmentation
Nuren Zhaksylyk, Ibrahim Almakky, Jay Paranjape +4
Accurate surgical instrument segmentation is essential in cataract surgery for tasks such as skill assessment and workflow optimization. However, limited annotated data makes it di…
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…
AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation
Jay N. Paranjape, Nithin Gopalakrishnan Nair, Shameema Sikder +2
Segmentation is a fundamental problem in surgical scene analysis using artificial intelligence. However, the inherent data scarcity in this domain makes it challenging to adapt tra…
Cross-Dataset Adaptation for Instrument Classification in Cataract Surgery Videos
Jay N. Paranjape, Shameema Sikder, Vishal M. Patel +1
Surgical tool presence detection is an important part of the intra-operative and post-operative analysis of a surgery. State-of-the-art models, which perform this task well on a pa…
Characterizing the Weight Space for Different Learning Models
Saurav Musunuru, Jay N. Paranjape, Rahul Kumar Dubey +1
Deep Learning has become one of the primary research areas in developing intelligent machines. Most of the well-known applications (such as Speech Recognition, Image Processing and…
Exploring the role of Input and Output Layers of a Deep Neural Network in Adversarial Defense
Jay N. Paranjape, Rahul Kumar Dubey, Vijendran V Gopalan
Deep neural networks are learning models having achieved state of the art performance in many fields like prediction, computer vision, language processing and so on. However, it ha…