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most citedRemoval of Batch Effects using Generative Adversarial Networks

2 citations · 2 across the 2 of their papers we have counts for

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Showing 2019Show all

6 papers · 1 filter

cs.CV2019

Multi-Person 3D Human Pose Estimation from Monocular Images

Rishabh Dabral, Nitesh B Gundavarapu, Rahul Mitra +3

Multi-person 3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data. We propose HG-RCNN, a…

cs.CV2019

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…

cs.CV2019

Progression Modelling for Online and Early Gesture Detection

Vikram Gupta, Sai Kumar Dwivedi, Rishabh Dabral +1

Online and Early detection of gestures is crucial for building touchless gesture based interfaces. These interfaces should operate on a stream of video frames instead of the comple…

cs.CV2019

Multiview-Consistent Semi-Supervised Learning for 3D Human Pose Estimation

Rahul Mitra, Nitesh B. Gundavarapu, Abhishek Sharma +1

The best performing methods for 3D human pose estimation from monocular images require large amounts of in-the-wild 2D and controlled 3D pose annotated datasets which are costly an…

cs.CV2019

Monocular 3D Human Pose Estimation by Generation and Ordinal Ranking

Saurabh Sharma, Pavan Teja Varigonda, Prashast Bindal +2

Monocular 3D human-pose estimation from static images is a challenging problem, due to the curse of dimensionality and the ill-posed nature of lifting 2D-to-3D. In this paper, we p…

cs.LG20192 cited

Removal of Batch Effects using Generative Adversarial Networks

Uddeshya Upadhyay, Arjun Jain

Many biological data analysis processes like Cytometry or Next Generation Sequencing (NGS) produce massive amounts of data which needs to be processed in batches for down-stream an…