22 citations · 70 across the 22 of their papers we have counts for
11 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…
Effective Evaluation of Deep Active Learning on Image Classification Tasks
Nathan Beck, Durga Sivasubramanian, Apurva Dani +2
With the goal of making deep learning more label-efficient, a growing number of papers have been studying active learning (AL) for deep models. However, there are a number of issue…
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…