facebookresearch/maskrcnn-benchmark: papers linking to it

GitHub · 9,360 stars · 2,447 forks · Python · license MIT · created 2018 · archived · github.com/facebookresearch/maskrcnn-benchmark

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to facebookresearch/maskrcnn-benchmark (https://github.com/facebookresearch/maskrcnn-benchmark), 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#

10 papers in the tracked journals link to the GitHub repository facebookresearch/maskrcnn-benchmark in their Crossref records, the first in 2019; most of them (4) in 2021.
By category: AI (10); the journals with the most are International Journal of Computer Vision (4), Pattern Recognition (3) and Applied Intelligence (2).

By year#

20192024

Papers linking to facebookresearch/maskrcnn-benchmark by publication year
Year	Papers
2019	1
2020	1
2021	4
2022	2
2023	1
2024	1

Journals#

Journals whose papers link to facebookresearch/maskrcnn-benchmark most
Journal	Papers
International Journal of Computer Vision	4
Pattern Recognition	3
Applied Intelligence	2
Journal Of Big Data	1

Papers linking to facebookresearch/maskrcnn-benchmark, most cited first
Published	Title	Journal	Authors	Citations	Link
2021-05-24	OCNet: Object Context for Semantic Segmentation	International Journal of Computer Vision	Yuhui Yuan, Lang Huang, Jianyuan Guo et al.	259	cites it
2019-08-07	Traffic flow estimation with data from a video surveillance camera	Journal Of Big Data	Aleksandr Fedorov, Kseniia Yu. Nikolskaia, Sergey A. Ivanov et al.	149	cites it
2022-10-02	Delving Deeper into Anti-Aliasing in ConvNets	International Journal of Computer Vision	Xueyan Zou, Fanyi Xiao, Zhiding Yu et al.	93	cites it
2021-04-19	Polysemy Deciphering Network for Robust Human–Object Interaction Detection	International Journal of Computer Vision	Xubin Zhong, Changxing Ding, Xian Qu et al.	60	cites it
2020-04-02	Graph-based neural networks for explainable image privacy inference	Pattern Recognition	Guang Yang, Juan Cao, Zhineng Chen et al.	47	cites it
2021-03-25	Recognition of visual-related non-driving activities using a dual-camera monitoring system	Pattern Recognition	Lichao Yang, Kuo Dong, Yan Ding et al.	23	cites it
2024-02-22	Learning to Generalize over Subpartitions for Heterogeneity-Aware Domain Adaptive Nuclei Segmentation	International Journal of Computer Vision	Jianan Fan, Dongnan Liu, Hang Chang et al.	13	cites it
2022-05-10	Residual objectness for imbalance reduction	Pattern Recognition	Joya Chen, Dong Liu, Bin Luo et al.	8	cites it
2021-10-23	Detection-by-tracking of traffic signs in videos	Applied Intelligence	Yanting Zhang, Zijian Wang, Ruoning Song et al.	7	cites it
2023-10-31	A broader study of cross-domain few-shot object detection	Applied Intelligence	Liangbing Sa, Chongchong Yu, Zhaorui Hong et al.	6	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). facebookresearch/maskrcnn-benchmark: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-facebookresearch-maskrcnn-benchmark

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

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