Search Results for author: Yuhao Liu

Found 13 papers, 3 papers with code

Neural Preset for Color Style Transfer

1 code implementation CVPR 2023 Zhanghan Ke, Yuhao Liu, Lei Zhu, Nanxuan Zhao, Rynson W. H. Lau

In this paper, we present a Neural Preset technique to address the limitations of existing color style transfer methods, including visual artifacts, vast memory requirement, and slow style switching speed.

Image Dehazing Image Harmonization +2

Gaussian Process-Gated Hierarchical Mixtures of Experts

1 code implementation9 Feb 2023 Yuhao Liu, Marzieh Ajirak, Petar Djuric

Further, the experts are also built with Gaussian processes and provide predictions that depend on test data.

Gaussian Processes Variational Inference

Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise

no code implementations7 Feb 2023 Teng Fu, Yuhao Liu, Jean Barbier, Marco Mondelli, Shansuo Liang, Tianqi Hou

We study the performance of a Bayesian statistician who estimates a rank-one signal corrupted by non-symmetric rotationally invariant noise with a generic distribution of singular values.

Sequential Estimation of Gaussian Process-based Deep State-Space Models

no code implementations29 Jan 2023 Yuhao Liu, Marzieh Ajirak, Petar Djuric

We consider the problem of sequential estimation of the unknowns of state-space and deep state-space models that include estimation of functions and latent processes of the models.

Gaussian Processes

Data-driven intelligent computational design for products: Method, techniques, and applications

no code implementations29 Jan 2023 Maolin Yang, Pingyu Jiang, Tianshuo Zang, Yuhao Liu

Data-driven intelligent computational design (DICD) is a research hotspot emerged under the context of fast-developing artificial intelligence.

Feature Engineering Retrieval

Structure-Informed Shadow Removal Networks

no code implementations9 Jan 2023 Yuhao Liu, Qing Guo, Lan Fu, Zhanghan Ke, Ke Xu, Wei Feng, Ivor W. Tsang, Rynson W. H. Lau

Extensive experiments on three shadow removal benchmarks demonstrate that our method outperforms existing shadow removal methods, and our StructNet can be integrated with existing methods to boost their performances further.

Shadow Removal

Hierarchical and Progressive Image Matting

no code implementations13 Oct 2022 Yu Qiao, Yuhao Liu, Ziqi Wei, Yuxin Wang, Qiang Cai, Guofeng Zhang, Xin Yang

In this paper, we propose an end-to-end Hierarchical and Progressive Attention Matting Network (HAttMatting++), which can better predict the opacity of the foreground from single RGB images without additional input.

Image Matting SSIM

Wider and Higher: Intensive Integration and Global Foreground Perception for Image Matting

no code implementations13 Oct 2022 Yu Qiao, Ziqi Wei, Yuhao Liu, Yuxin Wang, Dongsheng Zhou, Qiang Zhang, Xin Yang

This paper reviews recent deep-learning-based matting research and conceives our wider and higher motivation for image matting.

Image Matting

Prior-Induced Information Alignment for Image Matting

no code implementations28 Jun 2021 Yuhao Liu, Jiake Xie, Yu Qiao, Yong Tang and, Xin Yang

Image matting is an ill-posed problem that aims to estimate the opacity of foreground pixels in an image.

Image Matting

Multi-scale Information Assembly for Image Matting

no code implementations7 Jan 2021 Yu Qiao, Yuhao Liu, Qiang Zhu, Xin Yang, Yuxin Wang, Qiang Zhang, Xiaopeng Wei

Image matting is a long-standing problem in computer graphics and vision, mostly identified as the accurate estimation of the foreground in input images.

Image Matting

Tripartite Information Mining and Integration for Image Matting

1 code implementation ICCV 2021 Yuhao Liu, Jiake Xie, Xiao Shi, Yu Qiao, Yujie Huang, Yong Tang, Xin Yang

Regarding the nature of image matting, most researches have focused on solutions for transition regions.

Image Matting

Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors

no code implementations13 Oct 2018 Zhiwei Li, Huanfeng Shen, Qing Cheng, Yuhao Liu, Shucheng You, Zongyi He

In this paper, we propose a deep learning based cloud detection method named multi-scale convolutional feature fusion (MSCFF) for remote sensing images of different sensors.

Cloud Detection

Deep Structured Models For Group Activity Recognition

no code implementations12 Jun 2015 Zhiwei Deng, Mengyao Zhai, Lei Chen, Yuhao Liu, Srikanth Muralidharan, Mehrsan Javan Roshtkhari, Greg Mori

This paper presents a deep neural-network-based hierarchical graphical model for individual and group activity recognition in surveillance scenes.

Group Activity Recognition

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