14 citations · 46 across the 6 of their papers we have counts for
8 papers
VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation
Megh Shukla, Roshan Roy, Pankaj Singh +2
Advances in computing have enabled widespread access to pose estimation, creating new sources of data streams. Unlike mock set-ups for data collection, tapping into these data stre…
CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation
Midhun Vayyat, Jaswin Kasi, Anuraag Bhattacharya +2
In this work, we propose CLUDA, a simple, yet novel method for performing unsupervised domain adaptation (UDA) for semantic segmentation by incorporating contrastive losses into a…
A Mathematical Analysis of Learning Loss for Active Learning in Regression
Megh Shukla, Shuaib Ahmed
Active learning continues to remain significant in the industry since it is data efficient. Not only is it cost effective on a constrained budget, continuous refinement of the mode…
ProtoGAN: Towards Few Shot Learning for Action Recognition
Sai Kumar Dwivedi, Vikram Gupta, Rahul Mitra +2
Few-shot learning (FSL) for action recognition is a challenging task of recognizing novel action categories which are represented by few instances in the training data. In a more g…
Out-of-Distribution Detection for Generalized Zero-Shot Action Recognition
Devraj Mandal, Sanath Narayan, Saikumar Dwivedi +4
Generalized zero-shot action recognition is a challenging problem, where the task is to recognize new action categories that are unavailable during the training stage, in addition…
An Improved Learning Framework for Covariant Local Feature Detection
Nehal Doiphode, Rahul Mitra, Shuaib Ahmed +1
Learning feature detection has been largely an unexplored area when compared to handcrafted feature detection. Recent learning formulations use the covariant constraint in their lo…
A Large Dataset for Improving Patch Matching
Rahul Mitra, Nehal Doiphode, Utkarsh Gautam +4
We propose a new dataset for learning local image descriptors which can be used for significantly improved patch matching. Our proposed dataset consists of an order of magnitude mo…
Improved Descriptors for Patch Matching and Reconstruction
Rahul Mitra, Jiakai Zhang, Sanath Narayan +3
We propose a convolutional neural network (ConvNet) based approach for learning local image descriptors which can be used for significantly improved patch matching and 3D reconstru…