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Yihan Wu

22 papers hereh-index 10421 citations31 works total

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

author position
  • first author7
  • middle author15

Across the 22 of 22 papers where every author was matched, so the position is known.

fields
  • cs.CR9
  • cs.CL5
  • cs.LG4
  • cs.CV2
  • cs.SD1
  • eess.IV1
same name
  • Yihan Wu — 10 papers, h 10
  • Yihan Wu — 9 papers, h 5
  • Yihan Wu — 7 papers, h 7
  • Yihan Wu — 5 papers, h 14
  • Yihan Wu — 2 papers, h 6
  • Yihan Wu — 1 paper, 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
20232026
most citedGPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Dataset

4 citations · 13 across the 21 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

Hidden in Plain Tokens: Simply Robust, Gradient-Free Watermark for Synthetic Audio

Georgios Milis, Yubin Qin, Yihan Wu +1

As policy catches up with the capabilities of generative AI, watermarking is central to content provenance efforts. Inference-time watermarks for autoregressive models are unfit fo…

cs.LG2025

Model Correlation Detection via Random Selection Probing

Ruibo Chen, Sheng Zhang, Yihan Wu +3

The growing prevalence of large language models (LLMs) and vision-language models (VLMs) has heightened the need for reliable techniques to determine whether a model has been fine-…

cs.LG2025

Modality-Balancing Preference Optimization of Large Multimodal Models by Adversarial Negative Mining

Chenxi Liu, Tianyi Xiong, Yanshuo Chen +5

The task adaptation and alignment of Large Multimodal Models (LMMs) have been significantly advanced by instruction tuning and further strengthened by recent preference optimizatio…

cs.LG2024

Few-Shot Class Incremental Learning with Attention-Aware Self-Adaptive Prompt

Chenxi Liu, Zhenyi Wang, Tianyi Xiong +4

Few-Shot Class-Incremental Learning (FSCIL) models aim to incrementally learn new classes with scarce samples while preserving knowledge of old ones. Existing FSCIL methods usually…

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