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何翔宇
He Xiangyu (b. Dandong, Liaoning Province, 1986; lives and works in Beijing and Berlin) belongs to a new generation of Chinese conceptual artists who use a variety of media to articulate their cultural and social concerns. His ambitious and provocative works have quickly brought him international renown. This book is He Xiangyu's first monograph. With essays by Bao Dong, Li Zhenhua, Lu Mingjun, Sun Dongdong, and Wang Minan and a conversation between Li Zhenhua and the artist.
My Universe
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He Xiangyu
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He Xiangyu
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He Xiangyu
Conceptual artist He Xiangyu (*1986, China) has developed projects that take as their subject the goods and products that symbolize the mass production and consumption of the world while evoking the state of contemporary Chinese society. For his Coca-Cola Project, (2009 - 2012), the artist spent over a year simmering down a 127-ton batch of Coca-Cola, and transformed the black, charcoal-like substance that was extracted from this process into an apocalyptic installation. The Lemon Project, started in 2016, turns towards the practice of scientifi c research to produce an encyclopedic collection of the multitude of meanings and functions of lemons and the color yellow, leading him to immerse himself in the abyss of historical, psychological, medical, and cultural meanings associated with the color yellow. The book in a Japanese binding includes essays on the color yellow. On the inside of the uncut pages are more than 500 of He Xiangyu`s drawings entitled "Research on Yellow."
Computer Vision – ECCV 2022
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Visual Domain Adaptation in the Deep Learning Era
Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance/b>. While in many situations huge volumes of unlabeled data can be and often are generated and available, the cost of acquiring data labels remains high. Transfer learning (TL), and in particular domain adaptation (DA), has emerged as an effective solution to overcome the burden of annotation, exploiting the unlabeled data available from the target domain together with labeled data or pre-trained models from similar, yet different source domains. The aim of this book is to provide an overview of such DA/TL methods applied to computer vision, a field whose popula...
Computational, label, and data efficiency in deep learning for sparse 3D data
Deep learning is widely applied to sparse 3D data to perform challenging tasks, e.g., 3D object detection and semantic segmentation. However, the high performance of deep learning comes with high costs, including computational costs and the effort to capture and label data. This work investigates and improves the efficiency of deep learning for sparse 3D data to overcome the obstacles to the further development of this technology.
