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pro vyhledávání: '"Yang, Ge"'
Solving 3D medical inverse problems such as image restoration and reconstruction is crucial in modern medical field. However, the curse of dimensionality in 3D medical data leads mainstream volume-wise methods to suffer from high resource consumption
Externí odkaz:
http://arxiv.org/abs/2405.15241
Scene representations using 3D Gaussian primitives have produced excellent results in modeling the appearance of static and dynamic 3D scenes. Many graphics applications, however, demand the ability to manipulate both the appearance and the physical
Externí odkaz:
http://arxiv.org/abs/2404.01223
Autor:
Qiu, Ri-Zhao, Hu, Yafei, Yang, Ge, Song, Yuchen, Fu, Yang, Ye, Jianglong, Mu, Jiteng, Yang, Ruihan, Atanasov, Nikolay, Scherer, Sebastian, Wang, Xiaolong
An open problem in mobile manipulation is how to represent objects and scenes in a unified manner, so that robots can use it both for navigating in the environment and manipulating objects. The latter requires capturing intricate geometry while under
Externí odkaz:
http://arxiv.org/abs/2403.07563
Can we enable humanoid robots to generate rich, diverse, and expressive motions in the real world? We propose to learn a whole-body control policy on a human-sized robot to mimic human motions as realistic as possible. To train such a policy, we leve
Externí odkaz:
http://arxiv.org/abs/2402.16796
Unsupervised Domain Adaptation (UDA) is a learning technique that transfers knowledge learned in the source domain from labelled training data to the target domain with only unlabelled data. It is of significant importance to medical image segmentati
Externí odkaz:
http://arxiv.org/abs/2310.16665
High training costs of generative models and the need to fine-tune them for specific tasks have created a strong interest in model reuse and composition. A key challenge in composing iterative generative processes, such as GFlowNets and diffusion mod
Externí odkaz:
http://arxiv.org/abs/2309.16115
The scarcity of annotated data, particularly for rare diseases, limits the variability of training data and the range of detectable lesions, presenting a significant challenge for supervised anomaly detection in medical imaging. To solve this problem
Externí odkaz:
http://arxiv.org/abs/2308.15280
Deep neural networks are susceptible to adversarial examples, posing a significant security risk in critical applications. Adversarial Training (AT) is a well-established technique to enhance adversarial robustness, but it often comes at the cost of
Externí odkaz:
http://arxiv.org/abs/2308.02533
Self-supervised and language-supervised image models contain rich knowledge of the world that is important for generalization. Many robotic tasks, however, require a detailed understanding of 3D geometry, which is often lacking in 2D image features.
Externí odkaz:
http://arxiv.org/abs/2308.07931
Publikováno v:
IEEE Robotics and Automation Letters, 2024
Visual anomaly detection is essential and commonly used for many tasks in the field of computer vision. Recent anomaly detection datasets mainly focus on industrial automated inspection, medical image analysis and video surveillance. In order to broa
Externí odkaz:
http://arxiv.org/abs/2307.04956