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researcher

Yuan Wang

4 papers hereh-index 11 citations5 works total

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

author position
  • first author2
  • middle author2

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

fields
  • cs.CV2
  • cs.AI1
  • cs.CE1
same name
  • Yuan Wang — 43 papers, h 3
  • Yuan Wang — 11 papers, h 6
  • Yuan Wang — 8 papers, h 29
  • Yuan Wang — 8 papers, h 5
  • Yuan Wang — 8 papers, h 4
  • Yuan Wang — 8 papers, h 4

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

cs.CV2026

MedStreamBench: A Time-Aware Benchmark for Streaming and Proactive Medical Video Understanding

Yuan Wang, Shujian Gao, Songtao Jiang +2

Existing medical video benchmarks primarily evaluate whether a model produces the correct answer, but rarely assess whether it answers at the right time. In real clinical settings,…

cs.CE2026

AtomiMed: Hierarchical Atomic Fact-Checking for Universal Clinical-Aware Medical Report Evaluation

Yuan Wang, Wanxing Chang, Songtao Jiang +8

Traditional metrics for Medical Report Generation (MRG) predominantly rely on surface-level n-gram overlap, which fails to capture clinical factual accuracy and often overlooks cat…

cs.AI2026

Thinking with Deltas: Incentivizing Reinforcement Learning via Differential Visual Reasoning Policy

Shujian Gao, Yuan Wang, Jiangtao Yan +2

Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced reasoning capabilities in Large Language Models. However, adapting RLVR to multimodal domains suffe…

cs.CV2025

BARL: Bilateral Alignment in Representation and Label Spaces for Semi-Supervised Volumetric Medical Image Segmentation

Shujian Gao, Yuan Wang, Zekuan Yu

Semi-supervised medical image segmentation (SSMIS) seeks to match fully supervised performance while sharply reducing annotation cost. Mainstream SSMIS methods rely on \emph{label-…

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