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Peng Jin

Peng Jin has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining, Vision & language; often with Li Yuan, Bin Zhu, Hao Li.

2 papers in our library · 2023–2024

OpenAlex · h-index 1 · 1 works · 44 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 289 times in our library · ⭐ Paper Stars ranking: #3240

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

🎓Peking UniversityBeijing · China2025 · 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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksArts and Humanities · 1 workEngineering · 1 workControl and Systems Engineering · 1 workLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workVision & language · 1 work

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Video-LLaVA: Learning United Visual Representation by Alignment Before Projection (2023 · 627 citations)
    Video-LLaVA targets a weakness of earlier vision-language assistants. Images and videos were usually encoded into different feature spaces and then pushed into an LLM through separate or weak projection layers. The authors argue that this "misalignment before projection" makes it hard for the LLM to learn how the two…
  2. LLaVA-CoT: Let Vision Language Models Reason Step-by-Step (2024 · 138 citations)
    Vision-language models (VLMs) often answer complex visual questions without first organizing the task. Even with chain-of-thought prompting, they tend to commit to an answer early and then rationalize it, which produces errors and hallucinations. LLaVA-CoT addresses this by training a VLM to reason through a fixed…

 Co-authors here

4 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 289 times (of the 149,439 papers whose bibliographies we read); 70 citing papers with a page here →.

Updated

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