1 code implementation • 4 Apr 2024 • Ziyao Zeng, Daniel Wang, Fengyu Yang, Hyoungseob Park, Yangchao Wu, Stefano Soatto, Byung-Woo Hong, Dong Lao, Alex Wong
To test this, we focus on monocular depth estimation, the problem of predicting a dense depth map from a single image, but with an additional text caption describing the scene.
no code implementations • 15 Jan 2023 • Sihan Wang, Fuping Wu, Lei LI, Zheyao Gao, Byung-Woo Hong, Xiahai Zhuang
In this work, we propose an unsupervised framework for multi-class segmentation with both intensity and shape constraints.
1 code implementation • 30 Mar 2022 • Tian Yu Liu, Parth Agrawal, Allison Chen, Byung-Woo Hong, Alex Wong
In the absence of ground truth for model selection and training, our method, termed Monitored Distillation, allows a student to exploit a blind ensemble of teachers by selectively learning from predictions that best minimize the reconstruction error for a given image.
1 code implementation • 18 Sep 2021 • Alex Wong, Allison Chen, Yangchao Wu, Safa Cicek, Alexandre Tiard, Byung-Woo Hong, Stefano Soatto
We propose a neural network architecture in the form of a standard encoder-decoder where predictions are guided by a spatial expansion embedding network.
1 code implementation • 6 Jun 2021 • Alex Wong, Xiaohan Fei, Byung-Woo Hong, Stefano Soatto
We present a method to infer a dense depth map from a color image and associated sparse depth measurements.
no code implementations • 1 May 2021 • Kensuke Nakamura, Simon Korman, Byung-Woo Hong
Based on these observations, we propose a data representation for the GAN training, called noisy scale-space (NSS), that recursively applies the smoothing with a balanced noise to data in order to replace the high-frequency information by random data, leading to a coarse-to-fine training of GANs.
no code implementations • ICCV 2021 • Dahye Kim, Byung-Woo Hong
We present an image segmentation algorithm that is developed in an unsupervised deep learning framework.
no code implementations • 21 Dec 2020 • Kensuke Nakamura, Bong-Soo Sohn, Kyoung-Jae Won, Byung-Woo Hong
The quantitative analysis is performed by comparing the behavior of the label noise, the example trimming, and the proposed algorithm.
no code implementations • 14 Apr 2020 • Kensuke Nakamura, Stefano Soatto, Byung-Woo Hong
We propose a first-order stochastic optimization algorithm incorporating adaptive regularization applicable to machine learning problems in deep learning framework.
no code implementations • 23 Jul 2019 • Junghee Cho, Junseok Kwon, Byung-Woo Hong
We present an adaptive regularization algorithm that can be effectively applied to the optimization problem in deep learning framework.
no code implementations • 21 Jul 2019 • Kensuke Nakamura, Byung-Woo Hong
Regularization in the optimization of deep neural networks is often critical to avoid undesirable over-fitting leading to better generalization of model.
1 code implementation • CVPR 2019 • Alex Wong, Byung-Woo Hong, Stefano Soatto
Supervised learning methods to infer (hypothesize) depth of a scene from a single image require costly per-pixel ground-truth.
no code implementations • 20 Nov 2017 • Kensuke Nakamura, Stefano Soatto, Byung-Woo Hong
We present a stochastic first-order optimization algorithm, named BCSC, that adds a cyclic constraint to stochastic block-coordinate descent.
no code implementations • CVPR 2017 • Naeemullah Khan, Byung-Woo Hong, Anthony Yezzi, Ganesh Sundaramoorthi
We formulate an energy for segmentation that is designed to have preference for segmenting the coarse over fine structure of the image, without smoothing across boundaries of regions.
no code implementations • 9 May 2017 • Byung-Woo Hong, Ja-Keoung Koo, Martin Burger, Stefano Soatto
We present an adaptive regularization scheme for optimizing composite energy functionals arising in image analysis problems.
no code implementations • 27 Feb 2017 • Byung-Woo Hong, Ja-Keoung Koo, Stefano Soatto
We present a variational multi-label segmentation algorithm based on a robust Huber loss for both the data and the regularizer, minimized within a convex optimization framework.
no code implementations • 8 Sep 2016 • Byung-Woo Hong, Ja-Keoung Koo, Hendrik Dirks, Martin Burger
The desired properties, robustness and effectiveness, of the regularization parameter selection in a variational framework for imaging problems are achieved by merely replacing the static regularization parameter with our adaptive one.
no code implementations • 24 Mar 2016 • Ganesh Sundaramoorthi, Naeemullah Khan, Byung-Woo Hong
We formulate a general energy and method for segmentation that is designed to have preference for segmenting the coarse structure over the fine structure of the data, without smoothing across boundaries of regions.
no code implementations • CVPR 2014 • Ganesh Sundaramoorthi, Byung-Woo Hong
We derive an easy-to-implement and efficient algorithm for solving multi-label image partitioning problems in the form of the problem addressed by Region Competition.
no code implementations • 6 Feb 2014 • Omar Arif, Ganesh Sundaramoorthi, Byung-Woo Hong, Anthony Yezzi
We illustrate the use of this motion estimation scheme in propagating a segmentation across frames and show that it leads to more accurate segmentation than traditional motion estimation that does not make use of physical constraints.
no code implementations • CVPR 2013 • Byung-Woo Hong, Zhaojin Lu, Ganesh Sundaramoorthi
The advantage of this statistical model is that the underlying variables: the labels and the functions are less coupled than in the original formulation, and the labels can be computed from the functions with more global updates.