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pro vyhledávání: '"Zhang, Fangyi"'
Grasping compliant objects is difficult for robots - applying too little force may cause the grasp to fail, while too much force may lead to object damage. A robot needs to apply the right amount of force to quickly and confidently grasp the objects
Externí odkaz:
http://arxiv.org/abs/2312.14466
Autor:
Zhang, Fangyi, Corke, Peter
Finger-tip tactile sensors are increasingly used for robotic sensing to establish stable grasps and to infer object properties. Promising performance has been shown in a number of works for inferring adjectives that describe the object, but there rem
Externí odkaz:
http://arxiv.org/abs/2303.06656
Fabric manipulation is a long-standing challenge in robotics due to the enormous state space and complex dynamics. Learning approaches stand out as promising for this domain as they allow us to learn behaviours directly from data. Most prior methods
Externí odkaz:
http://arxiv.org/abs/2211.02832
Graph structure learning aims to learn connectivity in a graph from data. It is particularly important for many computer vision related tasks since no explicit graph structure is available for images for most cases. A natural way to construct a graph
Externí odkaz:
http://arxiv.org/abs/2210.03956
Publikováno v:
Shipin Kexue, Vol 45, Iss 4, Pp 135-143 (2024)
To investigate the preventive effect of Volvariella volvacea fruit body polypeptides (VVFP) on acute alcoholic liver injury in mice and its influence on the intestinal microbiota, VVFP (1–3 kDa molecular mass) which had been previously obtained by
Externí odkaz:
https://doaj.org/article/39e916430e7f4e33ac7d90c94bf16454
Autor:
Wang, Yaohua, Zhang, Yaobin, Zhang, Fangyi, Lin, Ming, Zhang, YuQi, Wang, Senzhang, Sun, Xiuyu
Face clustering has attracted rising research interest recently to take advantage of massive amounts of face images on the web. State-of-the-art performance has been achieved by Graph Convolutional Networks (GCN) due to their powerful representation
Externí odkaz:
http://arxiv.org/abs/2202.03800
Compression standards have been used to reduce the cost of image storage and transmission for decades. In recent years, learned image compression methods have been proposed and achieved compelling performance to the traditional standards. However, in
Externí odkaz:
http://arxiv.org/abs/2109.09280
Data augmentation is a commonly used approach to improving the generalization of deep learning models. Recent works show that learned data augmentation policies can achieve better generalization than hand-crafted ones. However, most of these works us
Externí odkaz:
http://arxiv.org/abs/2107.05384
In recent years, benefiting from the expressive power of Graph Convolutional Networks (GCNs), significant breakthroughs have been made in face clustering area. However, rare attention has been paid to GCN-based clustering on imbalanced data. Although
Externí odkaz:
http://arxiv.org/abs/2107.02477
Autor:
Fan, Cangning, Zhang, Fangyi, Liu, Peng, Sun, Xiuyu, Li, Hao, Xiao, Ting, Zhao, Wei, Tang, Xianglong
Previous transfer methods for anomaly detection generally assume the availability of labeled data in source or target domains. However, such an assumption is not valid in most real applications where large-scale labeled data are too expensive. Theref
Externí odkaz:
http://arxiv.org/abs/2105.06649