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20172021
most citedLIGHTEN: Learning Interactions with Graph and Hierarchical TEmporal Networks for HOI in videos

33 citations · 44 across the 5 of their papers we have counts for

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cs.CV202033 cited

LIGHTEN: Learning Interactions with Graph and Hierarchical TEmporal Networks for HOI in videos

Sai Praneeth Reddy Sunkesula, Rishabh Dabral, Ganesh Ramakrishnan

Analyzing the interactions between humans and objects from a video includes identification of the relationships between humans and the objects present in the video. It can be thoug…

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

Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity,Representation, Coverage and Importance

Vishal Kaushal, Rishabh Iyer, Khoshrav Doctor +6

This paper addresses automatic summarization of videos in a unified manner. In particular, we propose a framework for multi-faceted summarization for extractive, query base and ent…

cs.CV2019

Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision

Vishal Kaushal, Rishabh Iyer, Suraj Kothawade +3

Supervised machine learning based state-of-the-art computer vision techniques are in general data hungry. Their data curation poses the challenges of expensive human labeling, inad…

cs.CV2018

A Framework towards Domain Specific Video Summarization

Vishal Kaushal, Sandeep Subramanian, Suraj Kothawade +2

In the light of exponentially increasing video content, video summarization has attracted a lot of attention recently due to its ability to optimize time and storage. Characteristi…

cs.CV2018

Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks

Vishal Kaushal, Anurag Sahoo, Khoshrav Doctor +5

Supervised machine learning based state-of-the-art computer vision techniques are in general data hungry and pose the challenges of not having adequate computing resources and of h…