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Deep Chakraborty

University of Massachusetts Amherst

5 papers hereh-index 5281 citations6 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4
  • last author1

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.LG1
affiliations
  • University of Massachusetts Amherst
HomepageORCID 0000-0003-0950-4320
same name
  • Deep Chakraborty — 4 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182025
collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2025

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters

Shasvat Desai, Debasmita Ghose, Deep Chakraborty

Visual contrastive learning aims to learn representations by contrasting similar (positive) and dissimilar (negative) pairs of data samples. The design of these pairs significantly…

cs.CV2022

Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Images

Tejas Panambur, Deep Chakraborty, Melissa Meyer +3

Automatic terrain recognition in Mars rover images is an important problem not just for navigation, but for scientists interested in studying rock types, and by extension, conditio…

cs.CV2019

Pedestrian Detection in Thermal Images using Saliency Maps

Debasmita Ghose, Shasvat Mukeshkumar Desai, Sneha Bhattacharya +3

Thermal images are mainly used to detect the presence of people at night or in bad lighting conditions, but perform poorly at daytime. To solve this problem, most state-of-the-art…

cs.CV2018

Unsupervised Hard Example Mining from Videos for Improved Object Detection

SouYoung Jin, Aruni RoyChowdhury, Huaizu Jiang +4

Important gains have recently been obtained in object detection by using training objectives that focus on {\em hard negative} examples, i.e., negative examples that are currently…

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