Search Results for author: Bo Du

Found 62 papers, 27 papers with code

Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities

1 code implementation15 Apr 2022 Chuang Liu, Yibing Zhan, Chang Li, Bo Du, Jia Wu, Wenbin Hu, Tongliang Liu, DaCheng Tao

Graph neural networks have emerged as a leading architecture for many graph-level tasks such as graph classification and graph generation with a notable improvement.

Graph Classification Graph Generation

An Empirical Study of Remote Sensing Pretraining

1 code implementation6 Apr 2022 Di Wang, Jing Zhang, Bo Du, Gui-Song Xia, DaCheng Tao

To this end, we train different networks from scratch with the help of the largest RS scene recognition dataset up to now -- MillionAID, to obtain a series of RS pretrained backbones, including both convolutional neural networks (CNN) and vision transformers such as Swin and ViTAE, which have shown promising performance on computer vision tasks.

Change Detection Object Detection +2

An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection

1 code implementation1 Apr 2022 Jia Liu, Wenjie Xuan, Yuhang Gan, Juhua Liu, Bo Du

In this paper, we propose an end-to-end Supervised Domain Adaptation framework for cross-domain Change Detection, namely SDACD, to effectively alleviate the domain shift between bi-temporal images for better change predictions.

Change Detection Change detection for remote sensing images +1

Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers

1 code implementation5 Mar 2022 Lixiang Ru, Yibing Zhan, Baosheng Yu, Bo Du

Motivated by the inherent consistency between the self-attention in Transformers and the semantic affinity, we propose an Affinity from Attention (AFA) module to learn semantic affinity from the multi-head self-attention (MHSA) in Transformers.

Weakly-Supervised Semantic Segmentation

Multi-Tailed Vision Transformer for Efficient Inference

no code implementations3 Mar 2022 Yunke Wang, Bo Du, Chang Xu

To satisfy the sequential input of Transformer, the tail of ViT first splits each image into a sequence of visual tokens with a fixed length.

Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid Pooling

1 code implementation10 Feb 2022 Lixiang Ru, Bo Du, Yibing Zhan, Chen Wu

In the visual words learning module, we counter the first problem by enforcing the classification network to learn fine-grained visual word labels so that more object extents could be discovered.

Classification Weakly-Supervised Semantic Segmentation

Resistance Training using Prior Bias: toward Unbiased Scene Graph Generation

1 code implementation18 Jan 2022 Chao Chen, Yibing Zhan, Baosheng Yu, Liu Liu, Yong Luo, Bo Du

To address this problem, we propose Resistance Training using Prior Bias (RTPB) for the scene graph generation.

Graph Generation Unbiased Scene Graph Generation

Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis

1 code implementation13 Jan 2022 Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, Hua Jin, DaCheng Tao

To this end, we propose a knowledge graph augmented network (KGAN), which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information.

Aspect-Based Sentiment Analysis Knowledge Graphs +1

Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition

1 code implementation AAAI 2022 2021 Yue He, Chen Chen, Jing Zhang, Juhua Liu, Fengxiang He, Chaoyue Wang, Bo Du

Technically, given the character segmentation maps predicted by a VR model, we construct a subgraph for each instance, where nodes represent the pixels in it and edges are added between nodes based on their spatial similarity.

 Ranked #1 on Scene Text Recognition on SVT (using extra training data)

Language Modelling Scene Text Recognition

Expression is enough: Improving traffic signal control with advanced traffic state representation

1 code implementation19 Dec 2021 Liang Zhang, Qiang Wu, Jun Shen, Linyuan Lü, Jianqing Wu, Bo Du

Recently, finding fundamental properties for traffic state representation is more critical than complex algorithms for traffic signal control (TSC). In this paper, we (1) present a novel, flexible and straightforward method advanced max pressure (Advanced-MP), taking both running and queueing vehicles into consideration to decide whether to change current phase; (2) novelty design the traffic movement representation with the efficient pressure and effective running vehicles from Advanced-MP, namely advanced traffic state (ATS); (3) develop an RL-based algorithm template Advanced-XLight, by combining ATS with current RL approaches and generate two RL algorithms, "Advanced-MPLight" and "Advanced-CoLight".

Self-Ensembling GAN for Cross-Domain Semantic Segmentation

no code implementations15 Dec 2021 Yonghao Xu, Fengxiang He, Bo Du, Liangpei Zhang, DaCheng Tao

In SE-GAN, a teacher network and a student network constitute a self-ensembling model for generating semantic segmentation maps, which together with a discriminator, forms a GAN.

Semantic Segmentation

Binary Change Guided Hyperspectral Multiclass Change Detection

no code implementations8 Dec 2021 Meiqi Hu, Chen Wu, Bo Du, Liangpei Zhang

In this study, we proposed an unsupervised Binary Change Guided hyperspectral multiclass change detection Network (BCG-Net) for HMCD, which aims at boosting the multiclass change detection result and unmixing result with the mature binary change detection approaches.

Change Detection

Efficient Pressure: Improving efficiency for signalized intersections

1 code implementation4 Dec 2021 Qiang Wu, Liang Zhang, Jun Shen, Linyuan Lü, Bo Du, Jianqing Wu

Since conventional approaches could not adapt to dynamic traffic conditions, reinforcement learning (RL) has attracted more attention to help solve the traffic signal control (TSC) problem.

MixSiam: A Mixture-based Approach to Self-supervised Representation Learning

no code implementations4 Nov 2021 Xiaoyang Guo, Tianhao Zhao, Yutian Lin, Bo Du

In this way, the model could access more variant data samples of an instance and keep predicting invariant discriminative representations for them.

Contrastive Learning Representation Learning

Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis

1 code implementation26 Oct 2021 Juhua Liu, Qihuang Zhong, Liang Ding, Hua Jin, Bo Du, DaCheng Tao

In practice, we formulate the model pretrained on the sampled instances into a knowledge guidance model and a learner model, respectively.

Aspect-Based Sentiment Analysis Transfer Learning

SGEN: Single-cell Sequencing Graph Self-supervised Embedding Network

no code implementations15 Oct 2021 Ziyi Liu, Minghui Liao, Fulin Luo, Bo Du

This method constructs the graph by the similarity relationship between cells and adopts GCN to analyze the neighbor embedding information of samples, which makes the similar cell closer to each other on the 2D scatter plot.

Dimensionality Reduction Graph Embedding

Robust Weight Perturbation for Adversarial Training

no code implementations29 Sep 2021 Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Bo Du, Tongliang Liu

In this paper, we propose such a criterion, namely Loss Stationary Condition (LSC) for constrained perturbation.

Unsupervised Domain Adaptation for Semantic Segmentation via Low-level Edge Information Transfer

no code implementations18 Sep 2021 Hongruixuan Chen, Chen Wu, Yonghao Xu, Bo Du

To this end, a semantic-edge domain adaptation architecture is proposed, which uses an independent edge stream to process edge information, thereby generating high-quality semantic boundaries over the target domain.

Ranked #19 on Synthetic-to-Real Translation on GTAV-to-Cityscapes Labels (using extra training data)

Self-Supervised Learning Semantic Segmentation +2

Self-supervised Contrastive Learning for EEG-based Sleep Staging

1 code implementation16 Sep 2021 Xue Jiang, Jianhui Zhao, Bo Du, Zhiyong Yuan

In detail, the network's performance depends on the choice of transformations and the amount of unlabeled data used in the training process of self-supervised learning.

Contrastive Learning EEG +2

DeepFake MNIST+: A DeepFake Facial Animation Dataset

1 code implementation18 Aug 2021 Jiajun Huang, Xueyu Wang, Bo Du, Pei Du, Chang Xu

It includes 10, 000 facial animation videos in ten different actions, which can spoof the recent liveness detectors.

DeepFake Detection Face Swapping +1

Towards Deep and Efficient: A Deep Siamese Self-Attention Fully Efficient Convolutional Network for Change Detection in VHR Images

1 code implementation18 Aug 2021 Hongruixuan Chen, Chen Wu, Bo Du

With the goal of designing a quite deep architecture to obtain more precise CD results while simultaneously decreasing parameter numbers to improve efficiency, in this work, we present a very deep and efficient CD network, entitled EffCDNet.

Change Detection

Unsupervised Person Re-identification with Stochastic Training Strategy

1 code implementation16 Aug 2021 Tianyang Liu, Yutian Lin, Bo Du

State-of-the-art unsupervised re-ID methods usually follow a clustering-based strategy, which generates pseudo labels by clustering and maintains a memory to store instance features and represent the centroid of the clusters for contrastive learning.

Contrastive Learning Unsupervised Person Re-Identification

I3CL:Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection

1 code implementation3 Aug 2021 Bo Du, Jian Ye, Jing Zhang, Juhua Liu, DaCheng Tao

Existing methods for arbitrary-shaped text detection in natural scenes face two critical issues, i. e., 1) fracture detections at the gaps in a text instance; and 2) inaccurate detections of arbitrary-shaped text instances with diverse background context.

Scene Text Detection

Spectral-Spatial Graph Reasoning Network for Hyperspectral Image Classification

no code implementations26 Jun 2021 Di Wang, Bo Du, Liangpei Zhang

At last, by combining the extracted spatial and spectral graph contexts, we obtain the SSGRN to achieve a high accuracy classification.

Classification Hyperspectral Image Classification

Robust Self-Ensembling Network for Hyperspectral Image Classification

no code implementations8 Apr 2021 Yonghao Xu, Bo Du, Liangpei Zhang

Since the collection of pixel-level annotations for HSI is laborious and time-consuming, developing algorithms that can yield good performance in the small sample size situation is of great significance.

Classification General Classification +1

Transportation Density Reduction Caused by City Lockdowns Across the World during the COVID-19 Epidemic: From the View of High-resolution Remote Sensing Imagery

no code implementations2 Mar 2021 Chen Wu, Sihan Zhu, Jiaqi Yang, Meiqi Hu, Bo Du, Liangpei Zhang, Lefei Zhang, Chengxi Han, Meng Lan

Considering that public transportation was mostly reduced or even forbidden, our results indicate that city lockdown policies are effective at limiting human transmission within cities.

LocalDrop: A Hybrid Regularization for Deep Neural Networks

no code implementations1 Mar 2021 Ziqing Lu, Chang Xu, Bo Du, Takashi Ishida, Lefei Zhang, Masashi Sugiyama

In neural networks, developing regularization algorithms to settle overfitting is one of the major study areas.

A high-sensitivity fiber-coupled diamond magnetometer with surface coating

no code implementations24 Feb 2021 Shao-Chun Zhang, Hao-Bin Lin, Yang Dong, Bo Du, Xue-Dong Gao, Cui Yu, Zhi-Hong Feng, Xiang-Dong Chen, Guang-Can Guo, Fang-Wen Sun

Nitrogen-vacancy quantum defects in diamond offer a promising platform for magnetometry because of their remarkable optical and spin properties.

Applied Physics Quantum Physics

Channel Augmented Joint Learning for Visible-Infrared Recognition

no code implementations ICCV 2021 Mang Ye, Weijian Ruan, Bo Du, Mike Zheng Shou

This paper introduces a powerful channel augmented joint learning strategy for the visible-infrared recognition problem.

Data Augmentation Metric Learning

Not All Operations Contribute Equally: Hierarchical Operation-Adaptive Predictor for Neural Architecture Search

no code implementations ICCV 2021 Ziye Chen, Yibing Zhan, Baosheng Yu, Mingming Gong, Bo Du

Despite their efficiency, current graph-based predictors treat all operations equally, resulting in biased topological knowledge of cell architectures.

Neural Architecture Search

Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment

no code implementations ICCV 2021 Lin Zhang, Yong Luo, Yan Bai, Bo Du, Ling-Yu Duan

Federated Learning (FL) aims to establish a shared model across decentralized clients under the privacy-preserving constraint.

Federated Learning

Matrix Completion with Noise via Leveraged Sampling

no code implementations11 Nov 2020 Xinjian Huang, Weiwei Liu, Bo Du

This paper considers the recovery of a low-rank matrix, where some observed entries are sampled in a \emph{biased distribution} suitably dependent on \emph{leverage scores} of a matrix, and some observed entries are uniformly corrupted.

Matrix Completion

Hyperspectral Anomaly Change Detection Based on Auto-encoder

1 code implementation27 Oct 2020 Meiqi Hu, Chen Wu, Liangpei Zhang, Bo Du

In the ACDA model, two systematic auto-encoder (AE) networks are deployed to construct two predictors from two directions.

Change Detection

Semantic Change Detection with Asymmetric Siamese Networks

no code implementations12 Oct 2020 Kunping Yang, Gui-Song Xia, Zicheng Liu, Bo Du, Wen Yang, Marcello Pelillo, Liangpei Zhang

Given two multi-temporal aerial images, semantic change detection aims to locate the land-cover variations and identify their change types with pixel-wise boundaries.

Change Detection

Recurrent Feature Reasoning for Image Inpainting

1 code implementation CVPR 2020 Jingyuan Li, Ning Wang, Lefei Zhang, Bo Du, DaCheng Tao

To capture information from distant places in the feature map for RFR, we further develop KCA and incorporate it in RFR.

Image Inpainting SSIM

Designing and Training of A Dual CNN for Image Denoising

1 code implementation8 Jul 2020 Chunwei Tian, Yong Xu, WangMeng Zuo, Bo Du, Chia-Wen Lin, David Zhang

The enhancement block gathers and fuses the global and local features to provide complementary information for the latter network.

Image Denoising

An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images

no code implementations26 Jun 2020 Chen Wu, Yinong Guo, HaoNan Guo, Jingwen Yuan, Lixiang Ru, Hongruixuan Chen, Bo Du, Liangpei Zhang

The significant reduction and recovery of traffic density indicates that the lockdown policy in Wuhan show effectiveness in controlling human transmission inside the city, and the city returned to normal after lockdown lift.

Anomaly Detection Time Series

DSDANet: Deep Siamese Domain Adaptation Convolutional Neural Network for Cross-domain Change Detection

no code implementations16 Jun 2020 Hongruixuan Chen, Chen Wu, Bo Du, Liangpei Zhang

By optimizing the network parameters and kernel coefficients with the source labeled data and target unlabeled data, DSDANet can learn transferrable feature representation that can bridge the discrepancy between two domains.

Change Detection Domain Adaptation

Self-supervised Training of Graph Convolutional Networks

1 code implementation3 Jun 2020 Qikui Zhu, Bo Du, Pingkun Yan

Furthermore, the adjacency matrix is usually pre-defined and stationary, which makes the data augmentation strategies cannot be employed on the constructed graph structures data to augment the amount of training data.

Data Augmentation Self-Supervised Learning

Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion

1 code implementation3 Jun 2020 Lixiang Ru, Bo Du, Chen Wu

In this work, we proposed a CorrFusion module that fuses the highly correlated components in bi-temporal feature embeddings.

Change Detection General Classification +2

TextFuseNet: Scene Text Detection with Richer Fused Features

3 code implementations17 May 2020 Jian Ye, Zhe Chen, Juhua Liu, Bo Du

More specifically, we propose to perceive texts from three levels of feature representations, i. e., character-, word- and global-level, and then introduce a novel text representation fusion technique to help achieve robust arbitrary text detection.

Scene Text Detection

Deep Siamese Domain Adaptation Convolutional Neural Network for Cross-domain Change Detection in Multispectral Images

no code implementations13 Apr 2020 Hongruixuan Chen, Chen Wu, Bo Du, Liangepei Zhang

In this paper, we propose a novel deep siamese domain adaptation convolutional neural network (DSDANet) architecture for cross-domain change detection.

Change Detection Domain Adaptation

OASIS: One-pass aligned Atlas Set for Image Segmentation

no code implementations5 Dec 2019 Qikui Zhu, Bo Du, Pingkun Yan

Furthermore, instead of using image based similarity for label fusion, which can be distracted by the large background areas, we propose a novel strategy to compute the label similarity based weights for label fusion.

Image Registration Medical Image Segmentation +1

Multi-hop Convolutions on Weighted Graphs

1 code implementation12 Nov 2019 Qikui Zhu, Bo Du, Pingkun Yan

To address the above weaknesses, in this paper, we propose a new method of multi-hop convolutional network on weighted graphs.

Unified Multi-scale Feature Abstraction for Medical Image Segmentation

no code implementations24 Oct 2019 Xi Fang, Bo Du, Sheng Xu, Bradford J. Wood, Pingkun Yan

Automatic medical image segmentation, an essential component of medical image analysis, plays an importantrole in computer-aided diagnosis.

Medical Image Segmentation Semantic Segmentation

Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Networks

3 code implementations27 Jun 2019 Hongruixuan Chen, Chen Wu, Bo Du, Liangpei Zhang

Based on the unit two novel deep siamese convolutional neural networks, called as deep siamese multi-scale convolutional network (DSMS-CN) and deep siamese multi-scale fully convolutional network (DSMS-FCN), are designed for unsupervised and supervised change detection, respectively.

Change Detection

Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification

1 code implementation14 May 2019 Sheng Wan, Chen Gong, Ping Zhong, Bo Du, Lefei Zhang, Jian Yang

To alleviate this shortcoming, we consider employing the recently proposed Graph Convolutional Network (GCN) for hyperspectral image classification, as it can conduct the convolution on arbitrarily structured non-Euclidean data and is applicable to the irregular image regions represented by graph topological information.

Classification General Classification +1

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images

no code implementations8 Apr 2019 Lefei Zhang, Qian Zhang, Bo Du, Xin Huang, Yuan Yan Tang, DaCheng Tao

In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier.

Dimensionality Reduction General Classification +1

Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation

1 code implementation21 Feb 2019 Qikui Zhu, Bo Du, Pingkun Yan

To make the network more sensitive to the boundaries during segmentation, a boundary-weighted segmentation loss (BWL) is proposed.

Medical Image Segmentation Semantic Segmentation

Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images

no code implementations3 Dec 2018 Bo Du, Lixiang Ru, Chen Wu, Liangpei Zhang

In recent years, deep network has shown its brilliant performance in many fields including feature extraction and projection.

Change Detection

Defect Detection from UAV Images based on Region-Based CNNs

no code implementations23 Nov 2018 Meng Lan, YiPeng Zhang, Lefei Zhang, Bo Du

In this work, we study the performance of the region-based CNN for the electrical equipment defect detection by using the UAV images.

Defect Detection Object Detection

TLR: Transfer Latent Representation for Unsupervised Domain Adaptation

no code implementations19 Aug 2018 Pan Xiao, Bo Du, Jia Wu, Lefei Zhang, Ruimin Hu, Xuelong. Li

Many classic methods solve the domain adaptation problem by establishing a common latent space, which may cause the loss of many important properties across both domains.

Unsupervised Domain Adaptation

Unsupervised Domain Adaptive Re-Identification: Theory and Practice

3 code implementations30 Jul 2018 Liangchen Song, Cheng Wang, Lefei Zhang, Bo Du, Qian Zhang, Chang Huang, Xinggang Wang

We study the problem of unsupervised domain adaptive re-identification (re-ID) which is an active topic in computer vision but lacks a theoretical foundation.

General Classification Unsupervised Domain Adaptation

Deeply-Supervised CNN for Prostate Segmentation

no code implementations22 Mar 2017 Qikui Zhu, Bo Du, Baris Turkbey, Peter L . Choyke, Pingkun Yan

Prostate segmentation from Magnetic Resonance (MR) images plays an important role in image guided interven- tion.

A multi-task convolutional neural network for mega-city analysis using very high resolution satellite imagery and geospatial data

no code implementations26 Feb 2017 Fan Zhang, Bo Du, Liangpei Zhang

For the second target, a novel CNN-based universal framework is proposed to process the VHR satellite images and generate the land-use, urban density, and population distribution maps.

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