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pro vyhledávání: '"Cheng, Yanqi"'
Autor:
Essakine, Amer, Cheng, Yanqi, Cheng, Chun-Wun, Zhang, Lipei, Deng, Zhongying, Zhu, Lei, Schönlieb, Carola-Bibiane, Aviles-Rivero, Angelica I
Implicit Neural Representations (INRs) have emerged as a paradigm in knowledge representation, offering exceptional flexibility and performance across a diverse range of applications. INRs leverage multilayer perceptrons (MLPs) to model data as conti
Externí odkaz:
http://arxiv.org/abs/2411.03688
Autor:
Huang, Chaoyan, Wu, Zhongming, Cheng, Yanqi, Zeng, Tieyong, Schönlieb, Carola-Bibiane, Aviles-Rivero, Angelica I.
Image restoration is typically addressed through non-convex inverse problems, which are often solved using first-order block-wise splitting methods. In this paper, we consider a general type of non-convex optimisation model that captures many inverse
Externí odkaz:
http://arxiv.org/abs/2406.02458
Recent advances in deep learning have significantly improved brain tumour segmentation techniques; however, the results still lack confidence and robustness as they solely consider image data without biophysical priors or pathological information. In
Externí odkaz:
http://arxiv.org/abs/2403.09136
Autor:
Cheng, Yanqi, Zhang, Lipei, Shen, Zhenda, Wang, Shujun, Yu, Lequan, Chan, Raymond H., Schönlieb, Carola-Bibiane, Aviles-Rivero, Angelica I
Publikováno v:
Published in Transactions on Machine Learning Research, 2024
The utilisation of Plug-and-Play (PnP) priors in inverse problems has become increasingly prominent in recent years. This preference is based on the mathematical equivalence between the general proximal operator and the regularised denoiser, facilita
Externí odkaz:
http://arxiv.org/abs/2311.13682
Autor:
Shen, Zhenda, Cheng, Yanqi, Chan, Raymond H., Liò, Pietro, Schönlieb, Carola-Bibiane, Aviles-Rivero, Angelica I
Implicit neural representations (INRs) have garnered significant interest recently for their ability to model complex, high-dimensional data without explicit parameterisation. In this work, we introduce TRIDENT, a novel function for implicit neural r
Externí odkaz:
http://arxiv.org/abs/2311.13610
Autor:
Liu, Lihao, Cheng, Yanqi, Chen, Dongdong, He, Jing, Liò, Pietro, Schönlieb, Carola-Bibiane, Aviles-Rivero, Angelica I
Traffic videos inherently differ from generic videos in their stationary camera setup, thus providing a strong motion prior where objects often move in a specific direction over a short time interval. Existing works predominantly employ generic video
Externí odkaz:
http://arxiv.org/abs/2311.10092
Autor:
Cheng, Yanqi, Liu, Lihao, Wang, Shujun, Jin, Yueming, Schönlieb, Carola-Bibiane, Aviles-Rivero, Angelica I.
Surgical action triplet recognition provides a better understanding of the surgical scene. This task is of high relevance as it provides the surgeon with context-aware support and safety. The current go-to strategy for improving performance is the de
Externí odkaz:
http://arxiv.org/abs/2209.08647
Akademický článek
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Autor:
Cheng, Yanqi1 (AUTHOR), Sun, Ding2 (AUTHOR), Zou, Lu3 (AUTHOR), Li, Shaobin1 (AUTHOR), Tang, Ling1 (AUTHOR), Yu, Xiao2 (AUTHOR), Tang, Binqing4 (AUTHOR), Wu, Yingen2 (AUTHOR) wuyingen1940@163.com, Fang, Hong1 (AUTHOR) fanghong@shutcm.edu.cn
Publikováno v:
Chemical Biology & Drug Design. Nov2023, Vol. 102 Issue 5, p1034-1049. 16p.
Akademický článek
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