1 code implementation • 29 Mar 2023 • Weicheng Kuo, AJ Piergiovanni, Dahun Kim, Xiyang Luo, Ben Caine, Wei Li, Abhijit Ogale, Luowei Zhou, Andrew Dai, Zhifeng Chen, Claire Cui, Anelia Angelova
We propose a novel paradigm of training with a decoder-only model for multimodal tasks, which is surprisingly effective in jointly learning of these disparate vision-language tasks.
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no code implementations • 15 Oct 2021 • Pascal Tom Getreuer, Peyman Milanfar, Xiyang Luo
Partial differential equations (PDEs) are typically used as models of physical processes but are also of great interest in PDE-based image processing.
no code implementations • CVPR 2022 • Innfarn Yoo, Huiwen Chang, Xiyang Luo, Ondrej Stava, Ce Liu, Peyman Milanfar, Feng Yang
Digital watermarking is widely used for copyright protection.
no code implementations • 3 Aug 2020 • Xiyang Luo, Hossein Talebi, Feng Yang, Michael Elad, Peyman Milanfar
As a case study, we focus on the design of the quantization tables in the JPEG compression standard.
no code implementations • CVPR 2020 • Innfarn Yoo, Xiyang Luo, Yilin Wang, Feng Yang, Peyman Milanfar
DitherNet manipulates the input image to reduce color banding artifacts and provides an alternative to traditional dithering.
no code implementations • 1 Feb 2020 • Hossein Talebi, Damien Kelly, Xiyang Luo, Ignacio Garcia Dorado, Feng Yang, Peyman Milanfar, Michael Elad
In this work we aim to break the unholy connection between bit-rate and image quality, and propose a way to circumvent compression artifacts by pre-editing the incoming image and modifying its content to fit the given bits.
no code implementations • CVPR 2020 • Xiyang Luo, Ruohan Zhan, Huiwen Chang, Feng Yang, Peyman Milanfar
Watermarking is the process of embedding information into an image that can survive under distortions, while requiring the encoded image to have little or no perceptual difference from the original image.
1 code implementation • 10 Jul 2019 • Xing Liu, Masanori Suganuma, Xiyang Luo, Takayuki Okatani
The employment of convolutional neural networks has achieved unprecedented performance in the task of image restoration for a variety of degradation factors.
2 code implementations • 13 Feb 2019 • Zhijian Li, Xiyang Luo, Bao Wang, Andrea L. Bertozzi, Jack Xin
We study epidemic forecasting on real-world health data by a graph-structured recurrent neural network (GSRNN).
2 code implementations • ICLR 2019 • Irwan Bello, Sayali Kulkarni, Sagar Jain, Craig Boutilier, Ed Chi, Elad Eban, Xiyang Luo, Alan Mackey, Ofer Meshi
Ranking is a central task in machine learning and information retrieval.
1 code implementation • 17 Jun 2018 • Stanley Osher, Bao Wang, Penghang Yin, Xiyang Luo, Farzin Barekat, Minh Pham, Alex Lin
We propose a class of very simple modifications of gradient descent and stochastic gradient descent.
no code implementations • 2 Apr 2018 • Bao Wang, Xiyang Luo, Fangbo Zhang, Baichuan Yuan, Andrea L. Bertozzi, P. Jeffrey Brantingham
We present a generic framework for spatio-temporal (ST) data modeling, analysis, and forecasting, with a special focus on data that is sparse in both space and time.
no code implementations • 28 Feb 2018 • Alan Mackey, Xiyang Luo, Elad Eban
The maximization of many of these metrics can be expressed as a constrained optimization problem, where the constraint is a function of the classifier's predictions.
1 code implementation • NeurIPS 2018 • Bao Wang, Xiyang Luo, Zhen Li, Wei Zhu, Zuoqiang Shi, Stanley J. Osher
We replace the output layer of deep neural nets, typically the softmax function, by a novel interpolating function.
no code implementations • 26 Mar 2017 • Andrea L. Bertozzi, Xiyang Luo, Andrew M. Stuart, Konstantinos C. Zygalakis
In this paper we introduce, develop algorithms for, and investigate the properties of, a variety of Bayesian models for the task of binary classification; via the posterior distribution on the classification labels, these methods automatically give measures of uncertainty.