22 citations · 34 across the 9 of their papers we have counts for
10 papers · 1 filter
DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures
Suraj Kothawade, Akshit Srivastava, Venkat Iyer +2
Avoiding out-of-distribution (OOD) data is critical for training supervised machine learning models in the medical imaging domain. Furthermore, obtaining labeled medical data is di…
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification
Suraj Kothawade, Atharv Savarkar, Venkat Iyer +3
Training deep learning models on medical datasets that perform well for all classes is a challenging task. It is often the case that a suboptimal performance is obtained on some cl…
How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization
Vishal Kaushal, Suraj Kothawade, Anshul Tomar +2
Automatic video summarization is still an unsolved problem due to several challenges. The currently available datasets either have very short videos or have few long videos of only…
Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework
Vishal Kaushal, Suraj Kothawade, Rishabh Iyer +1
Automatic video summarization is still an unsolved problem due to several challenges. We take steps towards making automatic video summarization more realistic by addressing them.…
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…
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…