Search Results for author: Xiyang Luo

Found 15 papers, 5 papers with code

MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks

no code implementations29 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.

Cross-Modal Retrieval Image Retrieval +7

Solving Image PDEs with a Shallow Network

no code implementations15 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.

The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study

no code implementations3 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.


GIFnets: Differentiable GIF Encoding Framework

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.

Better Compression with Deep Pre-Editing

no code implementations1 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.

Distortion Agnostic Deep Watermarking

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.

Restoring Images with Unknown Degradation Factors by Recurrent Use of a Multi-branch Network

1 code implementation10 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.

Deblurring JPEG Artifact Removal +1

A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting

2 code implementations13 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).

Laplacian Smoothing Gradient Descent

1 code implementation17 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.

Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data

no code implementations2 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.

Constrained Classification and Ranking via Quantiles

no code implementations28 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.

Classification General Classification

Deep Neural Nets with Interpolating Function as Output Activation

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.

Uncertainty quantification in graph-based classification of high dimensional data

no code implementations26 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.

Binary Classification Classification +2

Cannot find the paper you are looking for? You can Submit a new open access paper.