open-mmlab/mmpretrain: papers linking to it

GitHub · 3,863 stars · 1,109 forks · Python · license Apache-2.0 · created 2020 · github.com/open-mmlab/mmpretrain

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to open-mmlab/mmpretrain (https://github.com/open-mmlab/mmpretrain), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

4 papers in the tracked journals link to the GitHub repository open-mmlab/mmpretrain in their Crossref records, the first in 2025; most of them (3) in 2025.
By category: AI (4); the journals with the most are Neural Networks (1), Neural Computing and Applications (1) and Pattern Recognition (1).

By year#

20252026

Papers linking to open-mmlab/mmpretrain by publication year
Year	Papers
2025	3
2026	1

Journals#

Journals whose papers link to open-mmlab/mmpretrain most
Journal	Papers
Neural Networks	1
Neural Computing and Applications	1
Pattern Recognition	1
International Journal of Computer Vision	1

Papers linking to open-mmlab/mmpretrain, most cited first
Published	Title	Journal	Authors	Citations	Link
2025-06-03	From ReLU to GeMU: Activation functions in the lens of cone projection	Neural Networks	Jiayun Li, Yuxiao Cheng, Yiwen Lu et al.	9	cites it
2025-07-18	Towards Balanced Representation Learning with Semantic Anchor Regularization	International Journal of Computer Vision	Chengjie Wang, Qiang Nie, Ying Chen et al.	0	cites it
2025-11-05	Enhancing local attention with global information interaction via progressive cluster propagation	Pattern Recognition	Jingfan Hang, Xianqiang Yang	0	cites it
2026-04-21	FineEdu: a fine-grained class students behavior understanding dataset with jointly action and attention annotations	Neural Computing and Applications	Zhijun Zhang, Zhijun Zhang, Ziyue Feng et al.	0	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). open-mmlab/mmpretrain: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-open-mmlab-mmpretrain

@misc{scholar4ai-code-github-open-mmlab-mmpretrain,
  author = {{Scholar4AI}},
  title = {open-mmlab/mmpretrain: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-open-mmlab-mmpretrain}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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