Qiang Li
Qiang Li has 3 papers in our library, in 2026, most of them on Language models & pretraining, Vision & language, Reasoning; often with Srikanth Patil, Suyash Mishra, Zhihua Wei.
OpenAlex · h-index 0 · 4 works · 0 citations OpenAlex, 2026-10-14
📄 3 works in our library · 🔗 Cited 1 time in our library · ⭐ Paper Stars ranking: #11394
Fields, topics and concepts
📚 Shelves 9The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksComputer Vision and Pattern Recognition · 3 worksHealth Informatics · 1 workMedicine · 1 workPharmacology · 1 workPharmacology, Toxicology and Pharmaceutics · 1 workArtificial Intelligence · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 work
🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksVision & language · 2 worksReasoning · 1 work
Works by year
2020s
Papers
- From Understanding to Engagement: Personalized pharmacy Video Clips via Vision Language Models (VLMs) (2026 · 1 citation)
Pharmaceutical companies produce many long videos, such as clinical trial interviews, seminars, manufacturing workflows and conference recordings. - Mitigating Action-Relation Hallucinations in LVLMs via Relation-aware Visual Enhancement (2026)
LVLMs often misdescribe what objects in an image are doing, for example claiming a woman rides a bicycle when she is actually pushing it. - Scaling Vision–Language Models for Pharmaceutical Long-Form Video Reasoning on Industrial GenAI Platform (2026)
Most benchmarks for vision–language models (VLMs) use short clips and assume ample compute. Pharmaceutical work is different: long trial recordings, lectures and patient-education videos must be processed under tight GPU, latency and compliance limits. The authors (Roche, Accenture, Involead) describe RICI, an…
Co-authors here
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2026
- Workshop on Advances in Language and Vision Research (ALVR): ALVR 2026
- Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual: Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual 2026
Cited by
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