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Zixin Chen

Zixin Chen has 2 papers in our library, in 2024–2025, most of them on Vision & language, Question answering, Sentiment & emotion; often with Hongzhan Lin, Jing Ma, KaShun Shum.

2 papers in our library · 2024–2025

OpenAlex · ORCID · h-index 2 · 8 works · 17 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 13 times in our library · ⭐ Paper Stars ranking: #8549

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

🎓Beijing University of Posts and TelecommunicationsBeijing · China2024–2025 · 4 paperscurrent🎓University of Nottingham Ningbo ChinaNingbo · 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 5The 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 workLanguage and Linguistics · 1 work

🧭 Topics 3Research subjects and languages the work is about.Vision & language · 2 worksSentiment & emotion · 1 workQuestion answering · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models (2024 · 20 citations)
    The paper tackles Multimodal Sarcasm Target Identification (MSTI), which means locating the mocked entity in a social-media post as both a text span and an image bounding box.
  2. Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answering (2025 · 9 citations)
    Charts can be designed to deceive, for example through truncated axes or selectively filtered data. Multimodal large language models (MLLMs) are good at reading charts, but no one had systematically tested whether they notice such manipulation. The authors address this with the Misleading ChartQA benchmark.

 Co-authors here

5 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 13 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated

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