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Saumya Gupta

Stony Brook University (Present)

4 papers hereh-index 8301 citations14 works total

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

author position
  • first author1
  • middle author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV3
  • eess.IV1
affiliations
  • Stony Brook University (Present)
  • Samsung Reserach & Development Institute (Past)
HomepageORCID 0000-0003-0933-3445
same name
  • Saumya Gupta — 4 papers, h 3
  • Saumya Gupta — 4 papers, h 3
  • Saumya Gupta — 2 papers, h 2
  • Saumya Gupta — 1 paper, h 2
  • Saumya Gupta — 1 paper, h 5

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

collaborators

4 papers

eess.IV2025

TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model

Meilong Xu, Saumya Gupta, Xiaoling Hu +5

Accurately modeling multi-class cell topology is crucial in digital pathology, as it provides critical insights into tissue structure and pathology. The synthetic generation of cel…

cs.CV2025

TopoDiffusionNet: A Topology-aware Diffusion Model

Saumya Gupta, Dimitris Samaras, Chao Chen

Diffusion models excel at creating visually impressive images but often struggle to generate images with a specified topology. The Betti number, which represents the number of stru…

cs.CV2025

Backdooring Vision-Language Models with Out-Of-Distribution Data

Weimin Lyu, Jiachen Yao, Saumya Gupta +6

The emergence of Vision-Language Models (VLMs) represents a significant advancement in integrating computer vision with Large Language Models (LLMs) to generate detailed text descr…

cs.CV2025

Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…

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