Search Results for author: Quan Zhang

Found 19 papers, 6 papers with code

Distilling Semantic Priors from SAM to Efficient Image Restoration Models

no code implementations25 Mar 2024 Quan Zhang, Xiaoyu Liu, Wei Li, Hanting Chen, Junchao Liu, Jie Hu, Zhiwei Xiong, Chun Yuan, Yunhe Wang

SPD leverages a self-distillation manner to distill the fused semantic priors to boost the performance of original IR models.

Deblurring Denoising +2

View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network

2 code implementations21 Mar 2024 Quan Zhang, Lei Wang, Vishal M. Patel, Xiaohua Xie, JianHuang Lai

Experiments on two datasets show that VDT is a feasible and effective solution for AGPReID, surpassing the previous method on mAP/Rank1 by up to 5. 0%/2. 7% on CARGO and 3. 7%/5. 2% on AG-ReID, keeping the same magnitude of computational complexity.

Person Re-Identification

MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object Detection

1 code implementation18 Feb 2024 Till Beemelmanns, Quan Zhang, Lutz Eckstein

Multi-modal 3D object detection models for automated driving have demonstrated exceptional performance on computer vision benchmarks like nuScenes.

3D Object Detection object-detection

Powerformer: A Section-adaptive Transformer for Power Flow Adjustment

no code implementations5 Jan 2024 KaiXuan Chen, Wei Luo, Shunyu Liu, Yaoquan Wei, Yihe Zhou, Yunpeng Qing, Quan Zhang, Jie Song, Mingli Song

In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power flow adjustment across different transmission sections.

HyperAttack: Multi-Gradient-Guided White-box Adversarial Structure Attack of Hypergraph Neural Networks

no code implementations24 Feb 2023 Chao Hu, Ruishi Yu, Binqi Zeng, Yu Zhan, Ying Fu, Quan Zhang, Rongkai Liu, Heyuan Shi

Hypergraph neural networks (HGNN) have shown superior performance in various deep learning tasks, leveraging the high-order representation ability to formulate complex correlations among data by connecting two or more nodes through hyperedge modeling.

Adversarial Attack

Optimization for Amortized Inverse Problems

no code implementations25 Oct 2022 Tianci Liu, Tong Yang, Quan Zhang, Qi Lei

Incorporating a deep generative model as the prior distribution in inverse problems has established substantial success in reconstructing images from corrupted observations.

Denoising

Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System

1 code implementation12 May 2022 KaiXuan Chen, Shunyu Liu, Na Yu, Rong Yan, Quan Zhang, Jie Song, Zunlei Feng, Mingli Song

As the topology of the power system is in the form of graph structure, graph neural network based representation learning is naturally suitable for learning the status of the power system.

Binary Classification Graph Representation Learning +1

Modeling 3D Layout for Group Re-Identification

1 code implementation CVPR 2022 Quan Zhang, Kaiheng Dang, Jian-Huang Lai, Zhanxiang Feng, Xiaohua Xie

To the best of our knowledge, 3DT is the first work to address GReID with 3D perspective, and the City1M is the currently largest dataset.

PANOM: Automatic Hyper-parameter Tuning for Inverse Problems

no code implementations NeurIPS Workshop Deep_Invers 2021 Tianci Liu, Quan Zhang, Qi Lei

Automated hyper-parameter tuning for unsupervised learning, including inverse problems, remains a long-standing open problem due to the lack of validation data.

Bilevel Optimization

Efficient QAM Signal Detector for Massive MIMO Systems via PS-ADMM Approach

no code implementations16 Apr 2021 Quan Zhang, Jiangtao Wang, Yongchao Wang

In this paper, we design an efficient quadrature amplitude modulation (QAM) signal detector for massive multiple-input multiple-output (MIMO) communication systems via the penalty-sharing alternating direction method of multipliers (PS-ADMM).

A Low-Complexity ADMM-based Massive MIMO Detectors via Deep Neural Networks

no code implementations27 Feb 2021 Isayiyas Nigatu Tiba, Quan Zhang, Jing Jiang, Yongchao Wang

An alternate direction method of multipliers (ADMM)-based detectors can achieve good performance in both small and large-scale multiple-input multiple-output (MIMO) systems.

Agent-Based Campus Novel Coronavirus Infection and Control Simulation

no code implementations22 Feb 2021 Pei Lv, Quan Zhang, Boya Xu, Ran Feng, Chaochao Li, Junxiao Xue, Bing Zhou, Mingliang Xu

Corona Virus Disease 2019 (COVID-19), due to its extremely high infectivity, has been spreading rapidly around the world and bringing huge influence to socioeconomic development as well as people's daily life.

Social and Information Networks Physics and Society Populations and Evolution

Designing Massive MIMO Detector via PS-ADMM approach

no code implementations14 Oct 2020 Quan Zhang, Yongchao Wang

Then, the higher modulation signals are decomposed into a sum of multiple binary variables through their inherent structures, by exploiting introduced binary variables as penalty functions, the detection optimization model is equivalent to a nonconvex sharing minimization problem.

MCMC-Interactive Variational Inference

no code implementations2 Oct 2020 Quan Zhang, Huangjie Zheng, Mingyuan Zhou

Leveraging well-established MCMC strategies, we propose MCMC-interactive variational inference (MIVI) to not only estimate the posterior in a time constrained manner, but also facilitate the design of MCMC transitions.

Variational Inference

Quantum-Classical Machine learning by Hybrid Tensor Networks

1 code implementation15 May 2020 Ding Liu, Zekun Yao, Quan Zhang

In this work, we propose the quantum-classical hybrid tensor networks (HTN) which combine tensor networks with classical neural networks in a uniform deep learning framework to overcome the limitations of regular tensor networks in machine learning.

BIG-bench Machine Learning Tensor Networks

RNN-Test: Towards Adversarial Testing for Recurrent Neural Network Systems

no code implementations11 Nov 2019 Jianmin Guo, Yue Zhao, Quan Zhang, Yu Jiang

Compared with the neuron coverage, the proposed state coverage metrics as guidance excel with 4. 17% to 97. 22% higher success (or generation) rate.

Language Modelling

Weibull Racing Survival Analysis with Competing Events, Left Truncation, and Time-varying Covariates

no code implementations2 Nov 2019 Quan Zhang, Qiang Gao, Mingfeng Lin, Mingyuan Zhou

Specifically, we study time to death of three types of lymphoma and show the potential of WDR in modeling nonlinear covariate effects and discovering new diseases.

Survival Analysis Methodology

Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks

1 code implementation NeurIPS 2018 Quan Zhang, Mingyuan Zhou

We propose Lomax delegate racing (LDR) to explicitly model the mechanism of survival under competing risks and to interpret how the covariates accelerate or decelerate the time to event.

Data Augmentation Survival Analysis

Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression

no code implementations30 Dec 2016 Quan Zhang, Mingyuan Zhou

To model categorical response variables given their covariates, we propose a permuted and augmented stick-breaking (paSB) construction that one-to-one maps the observed categories to randomly permuted latent sticks.

regression

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