activity
20202022
most citedYour Classifier can Secretly Suffice Multi-Source Domain Adaptation

21 citations · 41 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV20221 cited

Instantaneous Physiological Estimation using Video Transformers

Ambareesh Revanur, Ananyananda Dasari, Conrad S. Tucker +1

Video-based physiological signal estimation has been limited primarily to predicting episodic scores in windowed intervals. While these intermittent values are useful, they provide…

cs.CY2021

The First Vision For Vitals (V4V) Challenge for Non-Contact Video-Based Physiological Estimation

Ambareesh Revanur, Zhihua Li, Umur A. Ciftci +2

Telehealth has the potential to offset the high demand for help during public health emergencies, such as the COVID-19 pandemic. Remote Photoplethysmography (rPPG) - the problem of…

cs.CV202119 cited

Semi-Supervised Visual Representation Learning for Fashion Compatibility

Ambareesh Revanur, Vijay Kumar, Deepthi Sharma

We consider the problem of complementary fashion prediction. Existing approaches focus on learning an embedding space where fashion items from different categories that are visuall…

cs.LG202121 cited

Your Classifier can Secretly Suffice Multi-Source Domain Adaptation

Naveen Venkat, Jogendra Nath Kundu, Durgesh Kumar Singh +2

Multi-Source Domain Adaptation (MSDA) deals with the transfer of task knowledge from multiple labeled source domains to an unlabeled target domain, under a domain-shift. Existing m…

cs.LG2020

Class-Incremental Domain Adaptation

Jogendra Nath Kundu, Rahul Mysore Venkatesh, Naveen Venkat +2

We introduce a practical Domain Adaptation (DA) paradigm called Class-Incremental Domain Adaptation (CIDA). Existing DA methods tackle domain-shift but are unsuitable for learning…

cs.CV2020

Unsupervised Cross-Modal Alignment for Multi-Person 3D Pose Estimation

Jogendra Nath Kundu, Ambareesh Revanur, Govind Vitthal Waghmare +2

We present a deployment friendly, fast bottom-up framework for multi-person 3D human pose estimation. We adopt a novel neural representation of multi-person 3D pose which unifies t…