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diff --git a/site/content/pages/datasets/brainwash/index.md b/site/content/pages/datasets/brainwash/index.md index e6217a18..a2c13d8d 100644 --- a/site/content/pages/datasets/brainwash/index.md +++ b/site/content/pages/datasets/brainwash/index.md @@ -25,7 +25,7 @@ The Brainwash dataset is unique because it uses images from a publicly available Although Brainwash appears to be a less popular dataset, it was notably used in 2016 and 2017 by researchers affiliated with the National University of Defense Technology in China for two [research](https://www.semanticscholar.org/paper/Localized-region-context-and-object-feature-fusion-Li-Dou/b02d31c640b0a31fb18c4f170d841d8e21ffb66c) [projects](https://www.semanticscholar.org/paper/A-Replacement-Algorithm-of-Non-Maximum-Suppression-Zhao-Wang/591a4bfa6380c9fcd5f3ae690e3ac5c09b7bf37b) on advancing the capabilities of object detection to more accurately isolate the target region in an image. [^localized_region_context] [^replacement_algorithm] The [National University of Defense Technology](https://en.wikipedia.org/wiki/National_University_of_Defense_Technology) is controlled by China's top military body, the Central Military Commission. -The dataset also appears in a 2017 [research paper](https://ieeexplore.ieee.org/document/7877809) from Peking University for the purpose of improving surveillance capabilities for "people detection in the crowded scenes". +The Brainwash dataset also appears in a 2018 research paper affiliated with Megvii (Face++) that used images from Brainwash Cafe "to validate the generalization ability of [their] CrowdHuman dataset for head detection."[^crowdhuman]. Megvii is the parent company of Face++, who has provided surveillance technology to [monitor Uighur Muslims](https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html) in Xinjiang and may be [blacklisted](https://www.bloomberg.com/news/articles/2019-05-22/trump-weighs-blacklisting-two-chinese-surveillance-companies) in the United States.  @@ -47,3 +47,14 @@ The dataset also appears in a 2017 [research paper](https://ieeexplore.ieee.org/ [^end_to_end]: Stewart, Russel. Andriluka, Mykhaylo. "End-to-end people detection in crowded scenes". 2016. [^localized_region_context]: Li, Y. and Dou, Y. and Liu, X. and Li, T. Localized Region Context and Object Feature Fusion for People Head Detection. ICIP16 Proceedings. 2016. Pages 594-598. [^replacement_algorithm]: Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering. +[^crowdhuman]: Shuai Shao, Zijian Zhao, Boxun Li, Tete Xiao, Gang Yu, Xiangyu Zhang, and Jian Sun. CrowdHuman: Benchmark for Detecting Human in a Crowd. 2018. http://arxiv.org/abs/1805.00123 + journal = {CoRR}, + volume = {abs/1805.00123}, + year = {2018}, + url = {}, + archivePrefix = {arXiv}, + eprint = {1805.00123}, + timestamp = {Thu, 14 Mar 2019 14:56:07 +0100}, + biburl = {https://dblp.org/rec/bib/journals/corr/abs-1805-00123}, + bibsource = {dblp computer science bibliography, https://dblp.org} +}
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