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Ali Etemad

14 papers hereh-index 151k citations25 works total

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

author position
  • middle author3
  • last author11

Across the 14 of 14 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.LG4
  • eess.SP3
  • cs.HC2
  • cs.CL1
same name
  • Ali Etemad — 24 papers, h 22
  • Ali Etemad — 14 papers, h 7
  • Ali Etemad — 12 papers
  • Ali Etemad — 9 papers, h 4
  • Ali Etemad — 7 papers
  • Ali Etemad — 4 papers, h 6

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
20192026
most citedA Transformer Architecture for Stress Detection from ECG

59 citations · 62 across the 5 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2026

OpenMedReason: Scientific Reasoning Supervision for Medical Vision-Language Models

Negin Baghbanzadeh, Pritam Sarkar, Michael Colacci +6

High-stakes clinical use of large vision-language models (LVLMs) requires reasoning that is grounded in visual evidence and clinical knowledge, not just correct final answers. We i…

cs.CV2025

VCRBench: Exploring Long-form Causal Reasoning Capabilities of Large Video Language Models

Pritam Sarkar, Ali Etemad

Despite recent advances in video understanding, the capabilities of Large Video Language Models (LVLMs) to perform video-based causal reasoning remains underexplored, largely due t…

cs.CV2025

Self-alignment of Large Video Language Models with Refined Regularized Preference Optimization

Pritam Sarkar, Ali Etemad

Despite recent advances in Large Video Language Models (LVLMs), they still struggle with fine-grained temporal understanding, hallucinate, and often make simple mistakes on even si…

cs.CV2024

Mitigating Object Hallucination in MLLMs via Data-augmented Phrase-level Alignment

Pritam Sarkar, Sayna Ebrahimi, Ali Etemad +3

Despite their significant advancements, Multimodal Large Language Models (MLLMs) often generate factually inaccurate information, referred to as hallucination. In this work, we add…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.