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Shukang Yin

Shukang Yin has 2 papers in our library, in 2023, most of them on Language models & pretraining, Vision & language; often with Chaoyou Fu, Enhong Chen, Ke Li.

2 papers in our library · 2023

OpenAlex · ORCID · h-index 5 · 21 works · 1,010 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 304 times in our library · ⭐ Paper Stars ranking: #2272

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of Science and Technology of ChinaHefei · China2022–2026 · 10 paperscurrent🎓Nanjing Xiaozhuang UniversityNanjing · China2026 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksVision & language · 2 works

💡 Concepts 1Key ideas and methods explained on our concept pages.Large language models · 1 work

 Works by year

2020s

20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. A Survey on Multimodal Large Language Models (2023 · 1,069 citations)
    Yin and colleagues give one of the earliest overviews of Multimodal Large Language Models (MLLMs). These are systems that use a pre-trained LLM as the reasoning core and attach perception for images, video, audio and other signals. The authors argue that LLMs reason well but cannot see, and large vision models…
  2. Woodpecker: Hallucination Correction for Multimodal Large Language Models (2023 · 206 citations)
    Multimodal large language models (MLLMs) often describe things that are not in the image. They may invent objects or get attributes such as count or color wrong. Earlier fixes like LRV-Instruction and VIGC rely on instruction tuning, which needs new data and expensive retraining, and can make outputs shorter or…

 Co-authors here

8 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 304 times (of the 149,439 papers whose bibliographies we read); 86 citing papers with a page here →.

Updated

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