Search Results for author: Mingliang Xu

Found 47 papers, 11 papers with code

Dynamic Hypergraph Convolutional Networks for Skeleton-Based Action Recognition

no code implementations20 Dec 2021 Jinfeng Wei, Yunxin Wang, Mengli Guo, Pei Lv, Xiaoshan Yang, Mingliang Xu

Graph convolutional networks (GCNs) based methods have achieved advanced performance on skeleton-based action recognition task.

Action Recognition Skeleton Based Action Recognition

SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction

no code implementations5 Dec 2021 Pei Lv, Wentong Wang, Yunxin Wang, Yuzhen Zhang, Mingliang Xu, Changsheng Xu

In detail, when modeling social interaction, we propose a new \emph{social soft attention function}, which fully considers various interaction factors among pedestrians.

Autonomous Driving Pedestrian Trajectory Prediction +1

A Central Difference Graph Convolutional Operator for Skeleton-Based Action Recognition

1 code implementation13 Nov 2021 Shuangyan Miao, Yonghong Hou, Zhimin Gao, Mingliang Xu, Wanqing Li

This paper proposes a new graph convolutional operator called central difference graph convolution (CDGC) for skeleton based action recognition.

Action Recognition Skeleton Based Action Recognition

Contrastive Proposal Extension with LSTM Network for Weakly Supervised Object Detection

no code implementations14 Oct 2021 Pei Lv, Suqi Hu, Tianran Hao, Haohan Ji, Lisha Cui, Haoyi Fan, Mingliang Xu, Changsheng Xu

Inspired by the habit of observing things by the human, we propose a new method by comparing the initial proposals and the extension ones to optimize those initial proposals.

Multiple Instance Learning Weakly Supervised Object Detection

Point Cloud Pre-training by Mixing and Disentangling

no code implementations1 Sep 2021 Chao Sun, Zhedong Zheng, Xiaohan Wang, Mingliang Xu, Yi Yang

We hope this self-supervised learning attempt on point clouds can pave the way for reducing the deeply-learned model dependence on large-scale labeled data and saving a lot of annotation costs in the future.

Point Cloud Pre-training

Zero-sample surface defect detection and classification based on semantic feedback neural network

no code implementations15 Jun 2021 Yibo Guo, Yiming Fan, Zhiyang Xiang, Haidi Wang, Wenhua Meng, Mingliang Xu

Defect detection and classification technology has changed from traditional artificial visual inspection to current intelligent automated inspection, but most of the current defect detection methods are training related detection models based on a data-driven approach, taking into account the difficulty of collecting some sample data in the industrial field.

Defect Detection Image Classification +2

User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning

no code implementations14 Jun 2021 Pei Lv, Jianqi Fan, Xixi Nie, WeiMing Dong, Xiaoheng Jiang, Bing Zhou, Mingliang Xu, Changsheng Xu

This framework leverages user interactions to retouch and rank images for aesthetic assessment based on deep reinforcement learning (DRL), and generates personalized aesthetic distribution that is more in line with the aesthetic preferences of different users.

Image Enhancement

A self-adapting super-resolution structures framework for automatic design of GAN

no code implementations10 Jun 2021 Yibo Guo, Haidi Wang, Yiming Fan, Shunyao Li, Mingliang Xu

In this paper, we introduce a new super-resolution image reconstruction generative adversarial network framework, and a Bayesian optimization method used to optimizing the hyperparameters of the generator and discriminator.

Image Reconstruction Super-Resolution

Antagonistic Crowd Simulation Model Integrating Emotion Contagion and Deep Reinforcement Learning

no code implementations29 Apr 2021 Pei Lv, Boya Xu, Chaochao Li, Qingqing Yu, Bing Zhou, Mingliang Xu

In this paper, we propose one new antagonistic crowd simulation model by combing emotional contagion and deep reinforcement learning (ACSED).

Decision Making

Revisiting Deep Local Descriptor for Improved Few-Shot Classification

1 code implementation30 Mar 2021 Jun He, Richang Hong, Xueliang Liu, Mingliang Xu, Meng Wang

Few-shot classification studies the problem of quickly adapting a deep learner to understanding novel classes based on few support images.

Decision Making General Classification

Multi-Agent Path Planning based on MPC and DDPG

no code implementations26 Feb 2021 Junxiao Xue, Xiangyan Kong, Bowei Dong, Mingliang Xu

The problem of mixed static and dynamic obstacle avoidance is essential for path planning in highly dynamic environment.

Decision Making Unity

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

SiMaN: Sign-to-Magnitude Network Binarization

1 code implementation16 Feb 2021 Mingbao Lin, Rongrong Ji, Zihan Xu, Baochang Zhang, Fei Chao, Mingliang Xu, Chia-Wen Lin, Ling Shao

In this paper, we show that our weight binarization provides an analytical solution by encoding high-magnitude weights into +1s, and 0s otherwise.


Trear: Transformer-based RGB-D Egocentric Action Recognition

no code implementations5 Jan 2021 Xiangyu Li, Yonghong Hou, Pichao Wang, Zhimin Gao, Mingliang Xu, Wanqing Li

In this paper, we propose a \textbf{Tr}ansformer-based RGB-D \textbf{e}gocentric \textbf{a}ction \textbf{r}ecognition framework, called Trear.

Action Recognition Optical Flow Estimation

EC-DARTS: Inducing Equalized and Consistent Optimization Into DARTS

no code implementations ICCV 2021 Qinqin Zhou, Xiawu Zheng, Liujuan Cao, Bineng Zhong, Teng Xi, Gang Zhang, Errui Ding, Mingliang Xu, Rongrong Ji

EC-DARTS decouples different operations based on their categories to optimize the operation weights so that the operation gap between them is shrinked.

Transformer Guided Geometry Model for Flow-Based Unsupervised Visual Odometry

no code implementations8 Dec 2020 Xiangyu Li, Yonghong Hou, Pichao Wang, Zhimin Gao, Mingliang Xu, Wanqing Li

In this paper, we propose a method consisting of two camera pose estimators that deal with the information from pairwise images and a short sequence of images respectively.

Visual Odometry

An End-to-end Method for Producing Scanning-robust Stylized QR Codes

no code implementations16 Nov 2020 Hao Su, Jianwei Niu, Xuefeng Liu, Qingfeng Li, Ji Wan, Mingliang Xu, Tao Ren

Quick Response (QR) code is one of the most worldwide used two-dimensional codes.~Traditional QR codes appear as random collections of black-and-white modules that lack visual semantics and aesthetic elements, which inspires the recent works to beautify the appearances of QR codes.

Style Transfer

A Survey on Concept Factorization: From Shallow to Deep Representation Learning

no code implementations31 Jul 2020 Zhao Zhang, Yan Zhang, Mingliang Xu, Li Zhang, Yi Yang, Shuicheng Yan

In this paper, we therefore survey the recent advances on CF methodologies and the potential benchmarks by categorizing and summarizing the current methods.

Representation Learning

Memory-Augmented Relation Network for Few-Shot Learning

no code implementations9 May 2020 Jun He, Richang Hong, Xueliang Liu, Mingliang Xu, Zheng-Jun Zha, Meng Wang

Metric-based few-shot learning methods concentrate on learning transferable feature embedding that generalizes well from seen categories to unseen categories under the supervision of limited number of labelled instances.

Few-Shot Learning Metric Learning

Semi-DerainGAN: A New Semi-supervised Single Image Deraining Network

no code implementations23 Jan 2020 Yanyan Wei, Zhao Zhang, Yang Wang, Haijun Zhang, Mingbo Zhao, Mingliang Xu, Meng Wang

Although supervised deep deraining networks have obtained impressive results on synthetic datasets, they still cannot obtain satisfactory results on real images due to weak generalization of rain removal capacity, i. e., the pre-trained models usually cannot handle new shapes and directions that may lead to over-derained/under-derained results.

Single Image Deraining

DerainCycleGAN: Rain Attentive CycleGAN for Single Image Deraining and Rainmaking

no code implementations15 Dec 2019 Yanyan Wei, Zhao Zhang, Yang Wang, Mingliang Xu, Yi Yang, Shuicheng Yan, Meng Wang

However, in practice it is rather common to have no un-paired images in real deraining task, in such cases how to remove the rain streaks in an unsupervised way will be a very challenging task due to lack of constraints between images and hence suffering from low-quality recovery results.

Single Image Deraining Transfer Learning

Reinforcement Learning-based Visual Navigation with Information-Theoretic Regularization

1 code implementation9 Dec 2019 Qiaoyun Wu, Kai Xu, Jun Wang, Mingliang Xu, Dinesh Manocha

The regularization maximizes the mutual information between navigation actions and visual observation transforms of an agent, thus promoting more informed navigation decisions.


Kernelized Multiview Subspace Analysis by Self-weighted Learning

no code implementations23 Nov 2019 Huibing Wang, Yang Wang, Zhao Zhang, Xianping Fu, Zhuo Li, Mingliang Xu, Meng Wang

With the popularity of multimedia technology, information is always represented or transmitted from multiple views.

Dimensionality Reduction Image Retrieval

Personality-Aware Probabilistic Map for Trajectory Prediction of Pedestrians

no code implementations1 Nov 2019 Chaochao Li, Pei Lv, Mingliang Xu, Xinyu Wang, Dinesh Manocha, Bing Zhou, Meng Wang

We update this map dynamically based on the agents in the environment and prior trajectory of a pedestrian.

Trajectory Prediction

Multi-scale discriminative Region Discovery for Weakly-Supervised Object Localization

no code implementations24 Sep 2019 Pei Lv, Haiyu Yu, Junxiao Xue, Junjin Cheng, Lisha Cui, Bing Zhou, Mingliang Xu, Yi Yang

On ILSVRC 2016, the proposed method yields the Top-1 localization error of 48. 65\%, which outperforms previous results by 2. 75\%.

Weakly-Supervised Object Localization

Robust and Efficient Fuzzy C-Means Clustering Constrained on Flexible Sparsity

no code implementations19 Aug 2019 Jinglin Xu, Junwei Han, Mingliang Xu, Feiping Nie, Xuelong. Li

Clustering is an effective technique in data mining to group a set of objects in terms of some attributes.

Adaptive Exploration for Unsupervised Person Re-Identification

1 code implementation9 Jul 2019 Yuhang Ding, Hehe Fan, Mingliang Xu, Yi Yang

However, a problem of the adaptive selection is that, when an image has too many neighborhoods, it is more likely to attract other images as its neighborhoods.

Unsupervised Person Re-Identification

Predicting the Results of LTL Model Checking using Multiple Machine Learning Algorithms

no code implementations23 Jan 2019 Weijun Zhu, Mingliang Xu, Jianwei Wang

In this paper, we study how to predict the results of LTL model checking using some machine learning algorithms.

Abnormal Event Detection and Location for Dense Crowds using Repulsive Forces and Sparse Reconstruction

no code implementations21 Aug 2018 Pei Lv, Shunhua Liu, Mingliang Xu, Bing Zhou

This paper proposes a method based on repulsive forces and sparse reconstruction for the detection and location of abnormal events in crowded scenes.

Event Detection

MDSSD: Multi-scale Deconvolutional Single Shot Detector for Small Objects

1 code implementation18 May 2018 Lisha Cui, Rui Ma, Pei Lv, Xiaoheng Jiang, Zhimin Gao, Bing Zhou, Mingliang Xu

The performance of small object detection, however, is still less than satisfactory because of the deficiency of semantic information on shallow feature maps.

Small Object Detection

USAR: an Interactive User-specific Aesthetic Ranking Framework for Images

no code implementations3 May 2018 Pei Lv, Meng Wang, Yongbo Xu, Ze Peng, Junyi Sun, Shimei Su, Bing Zhou, Mingliang Xu

When assessing whether an image is of high or low quality, it is indispensable to take personal preference into account.

Bi-directional Graph Structure Information Model for Multi-Person Pose Estimation

no code implementations2 May 2018 Jing Wang, Ze Peng, Pei Lv, Junyi Sun, Bing Zhou, Mingliang Xu

The first branch predicts the confidence maps of joints and uses a geometrical transform kernel to propagate information between neighboring joints at the confidence level.

Multi-Person Pose Estimation

Depth Information Guided Crowd Counting for Complex Crowd Scenes

no code implementations3 Mar 2018 Mingliang Xu, Zhaoyang Ge, Xiaoheng Jiang, Gaoge Cui, Pei Lv, Bing Zhou, Changsheng Xu

DigCrowd first uses the depth information of an image to segment the scene into a far-view region and a near-view region.

Crowd Counting

Learning Correspondence Structures for Person Re-identification

no code implementations20 Mar 2017 Weiyao Lin, Yang shen, Junchi Yan, Mingliang Xu, Jianxin Wu, Jingdong Wang, Ke Lu

We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair.

Patch Matching Person Re-Identification

A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories

no code implementations10 Sep 2016 Weiyao Lin, Yang Zhou, Hongteng Xu, Junchi Yan, Mingliang Xu, Jianxin Wu, Zicheng Liu

Our approach first leverages the complete information from given trajectories to construct a thermal transfer field which provides a context-rich way to describe the global motion pattern in a scene.

3D Action Recognition Anomaly Detection

Tree-based Visualization and Optimization for Image Collection

no code implementations17 Jul 2015 Xintong Han, Chongyang Zhang, Weiyao Lin, Mingliang Xu, Bin Sheng, Tao Mei

The visualization of an image collection is the process of displaying a collection of images on a screen under some specific layout requirements.

Person Re-identification with Correspondence Structure Learning

1 code implementation ICCV 2015 Yang Shen, Weiyao Lin, Junchi Yan, Mingliang Xu, Jianxin Wu, Jingdong Wang

This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification.

Patch Matching Person Re-Identification

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