Zobrazeno 1 - 10
of 136
pro vyhledávání: '"Hong, Byung-Woo"'
Autor:
Zeng, Ziyao, Wang, Daniel, Yang, Fengyu, Park, Hyoungseob, Wu, Yangchao, Soatto, Stefano, Hong, Byung-Woo, Lao, Dong, Wong, Alex
Three-dimensional (3D) reconstruction from a single image is an ill-posed problem with inherent ambiguities, i.e. scale. Predicting a 3D scene from text description(s) is similarly ill-posed, i.e. spatial arrangements of objects described. We investi
Externí odkaz:
http://arxiv.org/abs/2404.03635
Cardiac segmentation is in great demand for clinical practice. Due to the enormous labor of manual delineation, unsupervised segmentation is desired. The ill-posed optimization problem of this task is inherently challenging, requiring well-designed c
Externí odkaz:
http://arxiv.org/abs/2301.06043
We propose a method to infer a dense depth map from a single image, its calibration, and the associated sparse point cloud. In order to leverage existing models (teachers) that produce putative depth maps, we propose an adaptive knowledge distillatio
Externí odkaz:
http://arxiv.org/abs/2203.16034
Autor:
Akhyar, Akhyar, Asyraf Zulkifley, Mohd, Lee, Jaesung, Song, Taekyung, Han, Jaeho, Cho, Chanhee, Hyun, Seunghyun, Son, Youngdoo, Hong, Byung-Woo
Publikováno v:
In Ecological Indicators June 2024 163
Autor:
Wong, Alex, Chen, Allison, Wu, Yangchao, Cicek, Safa, Tiard, Alexandre, Hong, Byung-Woo, Soatto, Stefano
We present a method to segment MRI scans of the human brain into ischemic stroke lesion and normal tissues. We propose a neural network architecture in the form of a standard encoder-decoder where predictions are guided by a spatial expansion embeddi
Externí odkaz:
http://arxiv.org/abs/2109.08791
We present a method to infer a dense depth map from a color image and associated sparse depth measurements. Our main contribution lies in the design of an annealing process for determining co-visibility (occlusions, disocclusions) and the degree of r
Externí odkaz:
http://arxiv.org/abs/2106.03010
Generative adversarial network (GAN) is a framework for generating fake data using a set of real examples. However, GAN is unstable in the training stage. In order to stabilize GANs, the noise injection has been used to enlarge the overlap of the rea
Externí odkaz:
http://arxiv.org/abs/2105.00220
Publikováno v:
In Pattern Recognition February 2024 146
Regularization is essential for avoiding over-fitting to training data in network optimization, leading to better generalization of the trained networks. The label noise provides a strong implicit regularization by replacing the target ground truth l
Externí odkaz:
http://arxiv.org/abs/2012.11073
We propose a first-order stochastic optimization algorithm incorporating adaptive regularization applicable to machine learning problems in deep learning framework. The adaptive regularization is imposed by stochastic process in determining batch siz
Externí odkaz:
http://arxiv.org/abs/2004.06341