Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Kaushik, Prakhar"'
Autor:
Fischer, Tom, Liu, Yaoyao, Jesslen, Artur, Ahmed, Noor, Kaushik, Prakhar, Wang, Angtian, Yuille, Alan, Kortylewski, Adam, Ilg, Eddy
Different from human nature, it is still common practice today for vision tasks to train deep learning models only initially and on fixed datasets. A variety of approaches have recently addressed handling continual data streams. However, extending th
Externí odkaz:
http://arxiv.org/abs/2407.09271
An important and unsolved problem in computer vision is to ensure that the algorithms are robust to changes in image domains. We address this problem in the scenario where we have access to images from the target domains but no annotations. Motivated
Externí odkaz:
http://arxiv.org/abs/2403.07277
We consider the problem of source-free unsupervised category-level pose estimation from only RGB images to a target domain without any access to source domain data or 3D annotations during adaptation. Collecting and annotating real-world 3D data and
Externí odkaz:
http://arxiv.org/abs/2401.10848
Autor:
Xu, Jiacong, Zhang, Yi, Peng, Jiawei, Ma, Wufei, Jesslen, Artur, Ji, Pengliang, Hu, Qixin, Zhang, Jiehua, Liu, Qihao, Wang, Jiahao, Ji, Wei, Wang, Chen, Yuan, Xiaoding, Kaushik, Prakhar, Zhang, Guofeng, Liu, Jie, Xie, Yushan, Cui, Yawen, Yuille, Alan, Kortylewski, Adam
Accurately estimating the 3D pose and shape is an essential step towards understanding animal behavior, and can potentially benefit many downstream applications, such as wildlife conservation. However, research in this area is held back by the lack o
Externí odkaz:
http://arxiv.org/abs/2308.11737
Catastrophic forgetting in neural networks is a significant problem for continual learning. A majority of the current methods replay previous data during training, which violates the constraints of an ideal continual learning system. Additionally, cu
Externí odkaz:
http://arxiv.org/abs/2102.11343