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Bo Zhao

Bo Zhao has 2 papers in our library, in 2025–2026, most of them on Evaluation & benchmarks, Information retrieval & search, Vision & language; often with Chen Jason Zhang, Defu Lian, Shitao Xiao.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 10 · 73 works · 329 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 16 times in our library · ⭐ Paper Stars ranking: #8683

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

🎓Sichuan UniversityChengdu · China2025 · 1 papercurrent🎓Tilburg UniversityTilburg · Netherlands2014–2021 · 12 papers🔬International Monetary FundWashington · United States2011–2022 · 5 papers🎓University of GroningenGroningen · Netherlands2009–2017 · 5 papers🎓Southwest UniversityChongqing · China2011–2015 · 5 papers🎓Shanghai Maritime UniversityShanghai · China2010–2012 · 4 papers🎓University of Hong KongPok Fu Lam · China2011–2021 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 7 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.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksComputer Vision and Pattern Recognition · 2 worksPsychology · 1 workSocial Psychology · 1 work

🧭 Topics 3Research subjects and languages the work is about.Information retrieval & search · 1 workVision & language · 1 workEvaluation & benchmarks · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. MegaPairs: Massive Data Synthesis for Universal Multimodal Retrieval (2025 · 26 citations)
    Universal multimodal retrievers, which must handle tasks such as composed image retrieval (CIR) beyond plain text-to-image matching, are held back by a shortage of instruction-tuning data.
  2. v-HUB: A Benchmark for Video Humor Understanding from Vision and Sound (2026)
    Most benchmarks for humor understanding rely on dialogue or text, so they say little about whether multimodal LLMs can see what makes a video funny.

 Co-authors here

9 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 16 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

Updated

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