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Jun Yu

Jun Yu has 4 papers in our library, in 2019–2026, most of them on Bias, fairness & safety, Named entity recognition, Language models & pretraining; often with Min Zhang, Daojing He, Jing Li.

4 papers in our library · 2019–2026

OpenAlex · ORCID · Wikidata · h-index 53 · 392 works · 14,044 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 194 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #4182

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

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 38 more institutions on single bylines are not listed.

 Fields, topics and concepts

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

 Works by year

2020s

2010s

 Papers

The most cited first (OpenAlex and our library).

  1. ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering (2019 · 403 citations)
    Video question answering (VideoQA) has had weaker benchmarks than image QA. Existing datasets tend to be small, rely on QA pairs generated automatically from captions, use very short clips, or cover only a few activity types. To address this, the authors built ActivityNet-QA, a fully human-annotated, open-ended…
  2. MTSA: Multi-turn Safety Alignment for LLMs through Multi-round Red-teaming (2025 · 14 citations)
    Jailbreak research and safety training have mostly targeted single-turn prompts. In multi-turn chats, an attacker can spread harmful intent across several innocent-looking turns, and LLMs are more likely to give in. MTSA is a two-stage framework for multi-turn safety.
  3. Safety Alignment via Constrained Knowledge Unlearning (2025 · 13 citations)
    Aligned large language models still produce harmful content under jailbreak attacks. The authors argue this happens because standard defenses, such as continued safety training, teach models to refuse without removing the dangerous knowledge itself, and they can also erode general skills. They propose Constrained…
  4. Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition (2026)
    Grounded Multimodal Named Entity Recognition (GMNER) asks a system to find named entities in a sentence, label their types, and link each one to a bounding box in the paired image, or mark it as absent from the image.

All works

6 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. Prompting Large Language Models with Answer Heuristics for Knowledge-Based Visual Question Answering ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetZhenwei Shao et al.2023 · 262 citations
  2. Deep Graph-neighbor Coherence Preserving Network for Unsupervised Cross-modal Hashing ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetJun Yu et al.2021 · 199 citations

 Co-authors here

12 of 18 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 194 times (of the 149,439 papers whose bibliographies we read); 30 citing papers with a page here →.

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