4 citations · 4 across the 6 of their papers we have counts for
7 papers
Conformal Prediction and MLLM aided Uncertainty Quantification in Scene Graph Generation
Sayak Nag, Udita Ghosh, Calvin-Khang Ta +3
Scene Graph Generation (SGG) aims to represent visual scenes by identifying objects and their pairwise relationships, providing a structured understanding of image content. However…
Uncertainty-Aware Diffusion Guided Refinement of 3D Scenes
Sarosij Bose, Arindam Dutta, Sayak Nag +4
Reconstructing 3D scenes from a single image is a fundamentally ill-posed task due to the severely under-constrained nature of the problem. Consequently, when the scene is rendered…
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation
Md Shazid Islam, Sayak Nag, Arindam Dutta +4
Unsupervised domain adaptive segmentation typically relies on self-training using pseudo labels predicted by a pre-trained network on an unlabeled target dataset. However, the nois…
Unbiased Scene Graph Generation in Videos
Sayak Nag, Kyle Min, Subarna Tripathi +1
The task of dynamic scene graph generation (SGG) from videos is complicated and challenging due to the inherent dynamics of a scene, temporal fluctuation of model predictions, and…
Reconstruction guided Meta-learning for Few Shot Open Set Recognition
Sayak Nag, Dripta S. Raychaudhuri, Sujoy Paul +1
In many applications, we are constrained to learn classifiers from very limited data (few-shot classification). The task becomes even more challenging if it is also required to ide…
Diversifying Support Vector Machines for Boosting using Kernel Perturbation: Applications to Class Imbalance and Small Disjuncts
Shounak Datta, Sayak Nag, Sankha Subhra Mullick +1
The diversification (generating slightly varying separating discriminators) of Support Vector Machines (SVMs) for boosting has proven to be a challenge due to the strong learning n…
Boosting with Lexicographic Programming: Addressing Class Imbalance without Cost Tuning
Shounak Datta, Sayak Nag, Swagatam Das
A large amount of research effort has been dedicated to adapting boosting for imbalanced classification. However, boosting methods are yet to be satisfactorily immune to class imba…