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Check FAQAbout Wenjun
Wenjun Chen, born 1985 in Guangzhou, China. He was photo editor and photographer in magazines before, and now he is a freelance photographer, videographer and co founder of May and June Studio, currently based in Guangzhou, China. His works focus on the relationship between individual situation and the social, cultural environment, and the communication between them. His works were the finalists of the Dummy Award Kassel 2016 and the 8th Three Shadow Photography Award 2016, they also received the Taipei Photo Emerging Talent Awards 2016, the XITEK EOS New Talent Award 2015 and Xu Xiaobing Photography Exhibition Outstanding Works of Art 2010. His works got exhibitions in China, Taiwan, Japan, UK, Germany, Italy, Ireland, Norway, Croatia, Portugal, Denmark and Slovakia. He had held self-publishing book workshops, talk and curatorial in China. http://chenwenjun.net/
English
Chinese (Mandarin)
Portfolio
Me and Me - Photographs and text by Wenjun Chen and Yanmei Jiang
Me and Me is a multimedia art project by Wenjun Chen and Yanmei Jiang that documents their life, growth, and creation through photography and other media from 2007 to 2015. The project includes self-portraits, group photos, two books titled In My Eyes, narrative letters, and social media clips, all compiled into a large handmade book showcasing their collaborative and individual work.
Leveraging Deep Neural Networks to Map Caribou Lichen in High-Resolution Satellite Images Based on a Small-Scale, Noisy UAV-Derived Map
The study investigates the use of deep neural networks to map caribou lichen in high-resolution satellite images using a small-scale, noisy UAV-derived map. The research demonstrates the potential of scaling up a very-high-spatial resolution lichen map from a small sample site to a much larger area using a semi-supervised learning approach. The methodology involves using a Teacher-Student framework to improve model performance with limited labeled data. The results show that the approach can achieve reasonable accuracy without collecting new samples, highlighting the robustness of the model against noisy labels and the benefits of using unlabeled data.
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Verified Mar 2018
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Mar 2018