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Sen Xing

Sen Xing has 2 papers in our library, in 2022–2023, most of them on Language models & pretraining, Multilingual & cross-lingual NLP; often with Guo Chen, Bin Li, Jilan Xu.

2 papers in our library · 2022–2023

OpenAlex · h-index 10 · 27 works · 926 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 444 times in our library · ⭐ Paper Stars ranking: #3243

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

🎓Chongqing UniversityChongqing · China2026 · 4 paperscurrent🎓Tsinghua UniversityBeijing · China2022–2026 · 4 paperscurrent🎓Beijing Forestry UniversityBeijing · China2023–2026 · 3 paperscurrent🔬Shanghai Artificial Intelligence LaboratoryShanghai · China2022–2024 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 8 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 3The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksMultilingual & cross-lingual NLP · 1 work

 Works by year

2020s

20231 paperSen Xing's papers of 202320221 paperSen Xing's papers of 2022

 Papers

The most cited first (OpenAlex and our library).

  1. InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks (2023 · 409 citations)
    Multimodal systems built on LLMs usually attach a vision encoder of roughly one billion parameters through a small, randomly initialized connector such as QFormer or a linear projection.
  2. InternVideo: General Video Foundation Models via Generative and Discriminative Learning (2022 · 128 citations)
    Most vision foundation models are pretrained and adapted at the image level. This makes them a poor fit for video tasks, which depend on motion and temporal structure. Existing video models also tend to specialize in either action understanding or video-language tasks. InternVideo is a family of general-purpose video…

 Co-authors here

12 of 15 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 444 times (of the 141,840 papers whose bibliographies we read); 26 citing papers with a page here →.

Updated

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