Search Results for author: Bo Jiang

Found 99 papers, 31 papers with code

A Spatial-Temporal Progressive Fusion Network for Breast Lesion Segmentation in Ultrasound Videos

no code implementations18 Mar 2024 Zhengzheng Tu, Zigang Zhu, Yayang Duan, Bo Jiang, Qishun Wang, Chaoxue Zhang

The main challenge for ultrasound video-based breast lesion segmentation is how to exploit the lesion cues of both intra-frame and inter-frame simultaneously.

Lesion Detection Lesion Segmentation +1

Long-term Frame-Event Visual Tracking: Benchmark Dataset and Baseline

3 code implementations9 Mar 2024 Xiao Wang, Ju Huang, Shiao Wang, Chuanming Tang, Bo Jiang, Yonghong Tian, Jin Tang, Bin Luo

Current event-/frame-event based trackers undergo evaluation on short-term tracking datasets, however, the tracking of real-world scenarios involves long-term tracking, and the performance of existing tracking algorithms in these scenarios remains unclear.

Object Tracking Rgb-T Tracking

Which Model to Transfer? A Survey on Transferability Estimation

no code implementations23 Feb 2024 Yuhe Ding, Bo Jiang, Aijing Yu, Aihua Zheng, Jian Liang

In this survey, we present the first review of existing advances in this area and categorize them into two separate realms: source-free model transferability estimation and source-dependent model transferability estimation.

Transfer Learning

VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning

no code implementations20 Feb 2024 Shaoyu Chen, Bo Jiang, Hao Gao, Bencheng Liao, Qing Xu, Qian Zhang, Chang Huang, Wenyu Liu, Xinggang Wang

Learning a human-like driving policy from large-scale driving demonstrations is promising, but the uncertainty and non-deterministic nature of planning make it challenging.

Autonomous Driving

Unifying Graph Contrastive Learning via Graph Message Augmentation

no code implementations8 Jan 2024 Ziyan Zhang, Bo Jiang, Jin Tang, Bin Luo

Based on the proposed GMA, we then propose a unified graph contrastive learning, termed Graph Message Contrastive Learning (GMCL), that employs attribution-guided universal GMA for graph contrastive learning.

Contrastive Learning Data Augmentation +2

Understanding Representation Learnability of Nonlinear Self-Supervised Learning

1 code implementation6 Jan 2024 Ruofeng Yang, Xiangyuan Li, Bo Jiang, Shuai Li

There are only a few theoretical works on data representation learnability, and many of those focus on final data representation, treating the nonlinear neural network as a ``black box".

Self-Supervised Learning

CRSOT: Cross-Resolution Object Tracking using Unaligned Frame and Event Cameras

1 code implementation5 Jan 2024 Yabin Zhu, Xiao Wang, Chenglong Li, Bo Jiang, Lin Zhu, Zhixiang Huang, Yonghong Tian, Jin Tang

In this work, we formally propose the task of object tracking using unaligned neuromorphic and visible cameras.

Object Tracking

Unleashing the Power of CNN and Transformer for Balanced RGB-Event Video Recognition

1 code implementation18 Dec 2023 Xiao Wang, Yao Rong, Shiao Wang, Yuan Chen, Zhe Wu, Bo Jiang, Yonghong Tian, Jin Tang

It is intuitive to combine them for high-performance RGB-Event based video recognition, however, existing works fail to achieve a good balance between the accuracy and model parameters, as shown in Fig.~\ref{firstimage}.

Video Recognition

Mathematical Language Models: A Survey

no code implementations12 Dec 2023 Wentao Liu, Hanglei Hu, Jie zhou, Yuyang Ding, Junsong Li, Jiayi Zeng, Mengliang He, Qin Chen, Bo Jiang, Aimin Zhou, Liang He

In recent years, there has been remarkable progress in leveraging Language Models (LMs), encompassing Pre-trained Language Models (PLMs) and Large-scale Language Models (LLMs), within the domain of mathematics.

Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoning

1 code implementation1 Dec 2023 Jiajun Cui, Minghe Yu, Bo Jiang, Aimin Zhou, Jianyong Wang, Wei zhang

Knowledge tracing (KT) plays a crucial role in computer-aided education and intelligent tutoring systems, aiming to assess students' knowledge proficiency by predicting their future performance on new questions based on their past response records.

counterfactual Counterfactual Reasoning +1

AnonPSI: An Anonymity Assessment Framework for PSI

no code implementations29 Nov 2023 Bo Jiang, Jian Du, Qiang Yan

We conducted a comprehensive performance evaluation of various attack strategies proposed utilizing two real datasets.

Robust Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation

no code implementations28 Nov 2023 Jiahui Wang, Qin Xu, Bo Jiang, Bin Luo

Label propagation methods try to propagate the labels of support samples on the constructed graph encoding the relationships between both support and query samples.

Few-Shot Learning

VcT: Visual change Transformer for Remote Sensing Image Change Detection

1 code implementation17 Oct 2023 Bo Jiang, Zitian Wang, Xixi Wang, Ziyan Zhang, Lan Chen, Xiao Wang, Bin Luo

Then, each pixel of feature map is regarded as a graph node and the graph neural network is proposed to model the structured information for coarse change map prediction.

Change Detection Representation Learning

Unleashing the power of Neural Collapse for Transferability Estimation

no code implementations9 Oct 2023 Yuhe Ding, Bo Jiang, Lijun Sheng, Aihua Zheng, Jian Liang

Transferability estimation aims to provide heuristics for quantifying how suitable a pre-trained model is for a specific downstream task, without fine-tuning them all.

Fairness Image Classification +3

Learning Point-wise Abstaining Penalty for Point Cloud Anomaly Detection

1 code implementation19 Sep 2023 Shaocong Xu, Pengfei Li, Xinyu Liu, Qianpu Sun, Yang Li, Shihui Guo, Zhen Wang, Bo Jiang, Rui Wang, Kehua Sheng, Bo Zhang, Hao Zhao

We demonstrate that learning different abstaining penalties, apart from point-wise penalty, for different types of (synthesized) outliers can further improve the performance.

Anomaly Detection Autonomous Driving +1

A study on the impact of pre-trained model on Just-In-Time defect prediction

1 code implementation5 Sep 2023 Yuxiang Guo, Xiaopeng Gao, Zhenyu Zhang, W. K. Chan, Bo Jiang

These findings emphasize the effectiveness of transformer-based pre-trained models in JIT defect prediction tasks, especially in scenarios with limited training data.

Defect Detection

Cerberus: A Deep Learning Hybrid Model for Lithium-Ion Battery Aging Estimation and Prediction Based on Relaxation Voltage Curves

no code implementations15 Aug 2023 Yue Xiang, Bo Jiang, Haifeng Dai

The degradation process of lithium-ion batteries is intricately linked to their entire lifecycle as power sources and energy storage devices, encompassing aspects such as performance delivery and cycling utilization.

Management

MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction

1 code implementation10 Aug 2023 Bencheng Liao, Shaoyu Chen, Yunchi Zhang, Bo Jiang, Qian Zhang, Wenyu Liu, Chang Huang, Xinggang Wang

We propose a unified permutation-equivalent modeling approach, \ie, modeling map element as a point set with a group of equivalent permutations, which accurately describes the shape of map element and stabilizes the learning process.

Autonomous Driving

SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event based Recognition

1 code implementation8 Aug 2023 Xiao Wang, Zongzhen Wu, Yao Rong, Lin Zhu, Bo Jiang, Jin Tang, Yonghong Tian

Secondly, they adopt either Spiking Neural Networks (SNN) for energy-efficient recognition with suboptimal results, or Artificial Neural Networks (ANN) for energy-intensive, high-performance recognition.

Thompson Sampling under Bernoulli Rewards with Local Differential Privacy

no code implementations3 Jul 2023 Bo Jiang, Tianchi Zhao, Ming Li

This paper investigates the problem of regret minimization for multi-armed bandit (MAB) problems with local differential privacy (LDP) guarantee.

Thompson Sampling

Point-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition

1 code implementation8 Jun 2023 Bo Jiang, Chengguo Yuan, Xiao Wang, Zhimin Bao, Lin Zhu, Yonghong Tian, Jin Tang

To address these issues, we propose a novel dual point-voxel absorbing graph representation learning for event stream data representation.

Event data classification Graph Representation Learning

AMatFormer: Efficient Feature Matching via Anchor Matching Transformer

no code implementations30 May 2023 Bo Jiang, Shuxian Luo, Xiao Wang, Chuanfu Li, Jin Tang

Second, AMatFormer adopts a shared FFN module to further embed the features of two images into the common domain and thus learn the consensus feature representations for the matching problem.

Prediction with Incomplete Data under Agnostic Mask Distribution Shift

no code implementations18 May 2023 Yichen Zhu, Jian Yuan, Bo Jiang, Tao Lin, Haiming Jin, Xinbing Wang, Chenghu Zhou

We focus on the case where the underlying joint distribution of complete features and label is invariant, but the missing pattern, i. e., mask distribution may shift agnostically between training and testing.

AGFormer: Efficient Graph Representation with Anchor-Graph Transformer

no code implementations12 May 2023 Bo Jiang, Fei Xu, Ziyan Zhang, Jin Tang, Feiping Nie

To alleviate the local receptive issue of GCN, Transformers have been exploited to capture the long range dependences of nodes for graph data representation and learning.

Implicit Bayes Adaptation: A Collaborative Transport Approach

no code implementations17 Apr 2023 Bo Jiang, Hamid Krim, Tianfu Wu, Derya Cansever

We integrate a metric correction term as well as a prior cluster structure in the source data of the OT-driven adaptation.

Unsupervised Domain Adaptation

VAD: Vectorized Scene Representation for Efficient Autonomous Driving

2 code implementations ICCV 2023 Bo Jiang, Shaoyu Chen, Qing Xu, Bencheng Liao, Jiajie Chen, Helong Zhou, Qian Zhang, Wenyu Liu, Chang Huang, Xinggang Wang

In this paper, we propose VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation.

Autonomous Driving Trajectory Planning

Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction

1 code implementation15 Mar 2023 Bencheng Liao, Shaoyu Chen, Bo Jiang, Tianheng Cheng, Qian Zhang, Wenyu Liu, Chang Huang, Xinggang Wang

We present a path-based online lane graph construction method, termed LaneGAP, which end-to-end learns the path and recovers the lane graph via a Path2Graph algorithm.

Autonomous Driving graph construction +1

MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection

1 code implementation9 Feb 2023 Yuhe Ding, Jian Liang, Bo Jiang, Aihua Zheng, Ran He

Existing cross-domain keypoint detection methods always require accessing the source data during adaptation, which may violate the data privacy law and pose serious security concerns.

Data Augmentation Keypoint Detection

Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric

2 code implementations20 Nov 2022 Chuanming Tang, Xiao Wang, Ju Huang, Bo Jiang, Lin Zhu, Jianlin Zhang, YaoWei Wang, Yonghong Tian

In this paper, we propose a single-stage backbone network for Color-Event Unified Tracking (CEUTrack), which achieves the above functions simultaneously.

Object Localization Object Tracking

Rethinking Batch Sample Relationships for Data Representation: A Batch-Graph Transformer based Approach

no code implementations19 Nov 2022 Xixi Wang, Bo Jiang, Xiao Wang, Bin Luo

(1) It employs a flexible graph model, termed Batch Graph to jointly encode the visual and semantic relationships of samples within each mini-batch.

Metric Learning

HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors

2 code implementations17 Nov 2022 Xiao Wang, Zongzhen Wu, Bo Jiang, Zhimin Bao, Lin Zhu, Guoqi Li, YaoWei Wang, Yonghong Tian

The main streams of human activity recognition (HAR) algorithms are developed based on RGB cameras which are suffered from illumination, fast motion, privacy-preserving, and large energy consumption.

Activity Prediction Human Activity Recognition +1

An efficient algorithm for the $\ell_{p}$ norm based metric nearness problem

no code implementations2 Nov 2022 Peipei Tang, Bo Jiang, Chengjing Wang

Due to the high memory requirement for the storage of the matrix related to the metric constraints, we take advantage of the special structure of the matrix and do not need to store the corresponding constraint matrix.

Flexible Alignment Super-Resolution Network for Multi-Contrast MRI

1 code implementation7 Oct 2022 Yiming Liu, Mengxi Zhang, Weiqin Zhang, Bo Jiang, Bo Hou, Dan Liu, Jie Chen, Heqing Lian

To tackle this problem, we propose the Flexible Alignment Super-Resolution Network (FASR-Net) for multi-contrast MRI Super-Resolution.

Super-Resolution

On Embeddings and Inverse Embeddings of Input Design for Regularized System Identification

no code implementations27 Sep 2022 Biqiang Mu, Tianshi Chen, He Kong, Bo Jiang, Lei Wang, Junfeng Wu

For the emerging regularized system identification, the study on input design has just started, and it is often formulated as a non-convex optimization problem that minimizes a scalar measure of the Bayesian mean squared error matrix subject to certain constraints, and the state-of-art method is the so-called quadratic mapping and inverse embedding (QMIE) method, where a time domain inverse embedding (TDIE) is proposed to find the inverse of the quadratic mapping.

Spiking GATs: Learning Graph Attentions via Spiking Neural Network

no code implementations5 Sep 2022 Beibei Wang, Bo Jiang

Graph Attention Networks (GATs) have been intensively studied and widely used in graph data learning tasks.

Graph Attention

DRSOM: A Dimension Reduced Second-Order Method

3 code implementations30 Jul 2022 Chuwen Zhang, Dongdong Ge, Chang He, Bo Jiang, Yuntian Jiang, Yinyu Ye

In this paper, we propose a Dimension-Reduced Second-Order Method (DRSOM) for convex and nonconvex (unconstrained) optimization.

HOPE: Hierarchical Spatial-temporal Network for Occupancy Flow Prediction

no code implementations21 Jun 2022 Yihan Hu, Wenxin Shao, Bo Jiang, Jiajie Chen, Siqi Chai, Zhening Yang, Jingyu Qian, Helong Zhou, Qiang Liu

In this report, we introduce our solution to the Occupancy and Flow Prediction challenge in the Waymo Open Dataset Challenges at CVPR 2022, which ranks 1st on the leaderboard.

Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search

1 code implementation19 May 2022 Xiao Wang, Zhe Chen, Bo Jiang, Jin Tang, Bin Luo, DaCheng Tao

To track the target in a video, current visual trackers usually adopt greedy search for target object localization in each frame, that is, the candidate region with the maximum response score will be selected as the tracking result of each frame.

Decision Making Image Captioning +5

Unified GCNs: Towards Connecting GCNs with CNNs

no code implementations26 Apr 2022 Ziyan Zhang, Bo Jiang, Bin Luo

Graph Convolutional Networks (GCNs) have been widely demonstrated their powerful ability in graph data representation and learning.

Few Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection

no code implementations CVPR 2022 Jingqun Tang, Wenqing Zhang, Hongye Liu, Mingkun Yang, Bo Jiang, Guanglong Hu, Xiang Bai

Different from previous approaches that learn robust deep representations of scene text in a holistic manner, our method performs scene text detection based on a few representative features, which avoids the disturbance by background and reduces the computational cost.

Ranked #21 on Object Detection In Aerial Images on DOTA (using extra training data)

object-detection Object Detection In Aerial Images +2

Refining Self-Supervised Learning in Imaging: Beyond Linear Metric

no code implementations25 Feb 2022 Bo Jiang, Hamid Krim, Tianfu Wu, Derya Cansever

We introduce in this paper a new statistical perspective, exploiting the Jaccard similarity metric, as a measure-based metric to effectively invoke non-linear features in the loss of self-supervised contrastive learning.

Contrastive Learning Self-Supervised Learning

Generalizing Aggregation Functions in GNNs:High-Capacity GNNs via Nonlinear Neighborhood Aggregators

no code implementations18 Feb 2022 Beibei Wang, Bo Jiang

(2) For max aggregator, it usually fails to be aware of the detailed information of node representations within neighborhood.

Graph Learning

GAMnet: Robust Feature Matching via Graph Adversarial-Matching Network

no code implementations MM 2021 Bo Jiang, Pengfei Sun, Ziyan Zhang, Jin Tang, Bin Luo

Also, GAMnet exploits sparse GM optimization as correspondence solver which is differentiable and can also incorporate discrete one-to-one matching constraints approximately in natural in the final matching prediction.

Ranked #7 on Graph Matching on PASCAL VOC (matching accuracy metric)

Graph Matching

Robust Graph Data Learning with Latent Graph Convolutional Representation

no code implementations29 Sep 2021 Bo Jiang, Ziyan Zhang, Bin Luo

Given an input graph $\textbf{A}$, LatGCR aims to generate a flexible latent graph $\tilde{\textbf{A}}$ for graph convolutional representation which obviously enhances the representation capacity and also performs robustly w. r. t graph structural attacks and noises.

Graph Learning

PRNet: A Progressive Regression Network for No-Reference User-Generated-Content Video Quality Assessment

no code implementations29 Sep 2021 Yang YangR, Bo Jiang, Kailin Wu

The largest UGC video dataset---YouTube-UGC still faces a problem that the database has right-skewed MOS distribution.

regression Video Quality Assessment +1

Human Pose Transfer with Augmented Disentangled Feature Consistency

no code implementations23 Jul 2021 Kun Wu, Chengxiang Yin, Zhengping Che, Bo Jiang, Jian Tang, Zheng Guan, Gangyi Ding

Deep generative models have made great progress in synthesizing images with arbitrary human poses and transferring poses of one person to others.

Data Augmentation Pose Transfer

MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking

2 code implementations22 Jul 2021 Xiao Wang, Xiujun Shu, Shiliang Zhang, Bo Jiang, YaoWei Wang, Yonghong Tian, Feng Wu

The visible and thermal filters will be used to conduct a dynamic convolutional operation on their corresponding input feature maps respectively.

Rgb-T Tracking

Feature importance recap and stacking models for forex price prediction

no code implementations6 Jul 2021 Yunze Li, Yanan Xie, Chen Yu, Fangxing Yu, Bo Jiang, Matloob Khushi

Traditionally, traders refer to technical analysis based on the historical data to make decisions and trade.

Feature Importance feature selection

A proximal-proximal majorization-minimization algorithm for nonconvex tuning-free robust regression problems

no code implementations25 Jun 2021 Peipei Tang, Chengjing Wang, Bo Jiang

In this paper, we introduce a proximal-proximal majorization-minimization (PPMM) algorithm for nonconvex tuning-free robust regression problems.

regression

Hierarchical Graph Attention Network for Few-Shot Visual-Semantic Learning

no code implementations ICCV 2021 Chengxiang Yin, Kun Wu, Zhengping Che, Bo Jiang, Zhiyuan Xu, Jian Tang

Deep learning has made tremendous success in computer vision, natural language processing and even visual-semantic learning, which requires a huge amount of labeled training data.

Graph Attention Image Captioning +2

Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction

no code implementations5 Nov 2020 Xuanzhao Wang, Zhengping Che, Bo Jiang, Ning Xiao, Ke Yang, Jian Tang, Jieping Ye, Jingyu Wang, Qi Qi

In this paper, we propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design which is more in line with the characteristics of surveillance videos.

Anomaly Detection Video Anomaly Detection

Fast Object Detection with Latticed Multi-Scale Feature Fusion

no code implementations5 Nov 2020 Yue Shi, Bo Jiang, Zhengping Che, Jian Tang

In this work, we present a novel module, the Fluff block, to alleviate drawbacks of current multi-scale fusion methods and facilitate multi-scale object detection.

Object object-detection +1

Visual Object Tracking by Segmentation with Graph Convolutional Network

no code implementations5 Sep 2020 Bo Jiang, Panpan Zhang, Lili Huang

The proposed model provides a general end-to-end framework which integrates i) label linear prediction, and ii) structure-aware feature information of each superpixel together to obtain object segmentation and further improves the performance of tracking.

Object Segmentation +3

WANA: Symbolic Execution of Wasm Bytecode for Cross-Platform Smart Contract Vulnerability Detection

1 code implementation30 Jul 2020 Dong Wang, Bo Jiang, W. K. Chan

Furthermore, WANA proposes a set of test oracles to detect the vulnerabilities in EOSIO and Ethereum smart contracts based on WebAssembly bytecode analysis.

Software Engineering D.2.5

EOSFuzzer: Fuzzing EOSIO Smart Contracts for Vulnerability Detection

1 code implementation29 Jul 2020 Yuhe Huang, Bo Jiang, W. K. Chan

Our fuzzing experiment on 3963 EOSIO smart contracts shows that EOSFuzzer is both effective and efficient to detect EOSIO smart contract vulnerabilities with high accuracy.

Software Engineering D.2.5

STADB: A Self-Thresholding Attention Guided ADB Network for Person Re-identification

1 code implementation7 Jul 2020 Bo Jiang, Sheng Wang, Xiao Wang, Aihua Zheng

Specifically, STADB first obtains an attention map by channel-wise pooling and returns a drop mask by thresholding the attention map.

Person Re-Identification

Incomplete Graph Representation and Learning via Partial Graph Neural Networks

no code implementations23 Mar 2020 Bo Jiang, Ziyan Zhang

To address this problem, we develop a novel partial aggregation based GNNs, named Partial Graph Neural Networks (PaGNNs), for attribute-incomplete graph representation and learning.

Attribute

\emph{cm}SalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks

1 code implementation21 Dec 2019 Bo Jiang, Zitai Zhou, Xiao Wang, Jin Tang, Bin Luo

Fusing complementary information of RGB and depth has been demonstrated to be effective for image salient object detection which is known as RGB-D salient object detection problem.

Edge Detection Generative Adversarial Network +6

AttKGCN: Attribute Knowledge Graph Convolutional Network for Person Re-identification

no code implementations24 Nov 2019 Bo Jiang, Xixi Wang, Jin Tang

Discriminative feature representation of person image is important for person re-identification (Re-ID) task.

Attribute Person Re-Identification

GLMNet: Graph Learning-Matching Networks for Feature Matching

no code implementations18 Nov 2019 Bo Jiang, Pengfei Sun, Jin Tang, Bin Luo

However, the matching graphs we feed to existing graph convolutional matching networks are generally fixed and independent of graph matching, which thus are not guaranteed to be optimal for the graph matching task.

Graph Learning Graph Matching +1

Context-Aware Graph Attention Networks

no code implementations4 Sep 2019 Bo Jiang, Leiling Wang, Jin Tang, Bin Luo

In particular, CaGAT conducts context-aware learning on both node feature representation and edge (weight) representation simultaneously and cooperatively in a unified manner which can boost their respective performance in network training.

Graph Attention

GmCN: Graph Mask Convolutional Network

no code implementations4 Sep 2019 Bo Jiang, Beibei Wang, Jin Tang, Bin Luo

Graph Convolutional Networks (GCNs) have shown very powerful for graph data representation and learning tasks.

Graph Learning

Semi-supervised Learning with Adaptive Neighborhood Graph Propagation Network

no code implementations14 Aug 2019 Bo Jiang, Leiling Wang, Jin Tang, Bin Luo

In this paper, we first re-interpret graph convolution operation in GCNs as a composition of feature propagation and (non-linear) transformation.

graph construction

PH-GCN: Person Re-identification with Part-based Hierarchical Graph Convolutional Network

no code implementations20 Jul 2019 Bo Jiang, Xixi Wang, Bin Luo

Given a person image, PH-GCN first constructs a hierarchical graph to represent the pairwise relationships among different parts.

Person Re-Identification

Data Representation and Learning With Graph Diffusion-Embedding Networks

no code implementations CVPR 2019 Bo Jiang, Doudou Lin, Jin Tang, Bin Luo

Recently, graph convolutional neural networks have been widely studied for graph-structured data representation and learning.

Semi-Supervised Learning With Graph Learning-Convolutional Networks

no code implementations CVPR 2019 Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang, Bin Luo

In this paper, we propose a novel Graph Learning-Convolutional Network (GLCN) for graph data representation and semi-supervised learning.

graph construction Graph Learning

Robust Graph Data Learning via Latent Graph Convolutional Representation

no code implementations26 Apr 2019 Bo Jiang, Ziyan Zhang, Bin Luo

Given an input graph $\textbf{A}$, LatGCR aims to generate a flexible latent graph $\widetilde{\textbf{A}}$ for graph convolutional representation which obviously enhances the representation capacity and also performs robustly w. r. t graph structural attacks and noises.

Graph Learning Node Classification +1

D$^2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios

no code implementations3 Apr 2019 Zhengping Che, Guangyu Li, Tracy Li, Bo Jiang, Xuefeng Shi, Xinsheng Zhang, Ying Lu, Guobin Wu, Yan Liu, Jieping Ye

Driving datasets accelerate the development of intelligent driving and related computer vision technologies, while substantial and detailed annotations serve as fuels and powers to boost the efficacy of such datasets to improve learning-based models.

MisGAN: Learning from Incomplete Data with Generative Adversarial Networks

1 code implementation ICLR 2019 Steven Cheng-Xian Li, Bo Jiang, Benjamin Marlin

Generative adversarial networks (GANs) have been shown to provide an effective way to model complex distributions and have obtained impressive results on various challenging tasks.

Abstract Argumentation

Multiple Graph Adversarial Learning

no code implementations22 Jan 2019 Bo Jiang, Ziyan Zhang, Jin Tang, Bin Luo

In this paper, we propose a novel Multiple Graph Adversarial Learning (MGAL) framework for multi-graph representation and learning.

Graph Learning-Convolutional Networks

no code implementations25 Nov 2018 Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang

Recently, graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks.

graph construction Graph Learning

Graph Diffusion-Embedding Networks

no code implementations1 Oct 2018 Bo Jiang, Doudou Lin, Jin Tang

We present a novel graph diffusion-embedding networks (GDEN) for graph structured data.

Graph Laplacian Regularized Graph Convolutional Networks for Semi-supervised Learning

2 code implementations26 Sep 2018 Bo Jiang, Doudou Lin

Recently, graph convolutional network (GCN) has been widely used for semi-supervised classification and deep feature representation on graph-structured data.

Classification Dimensionality Reduction +1

Towards Discrete Solution: A Sparse Preserving Method for Correspondence Problem

no code implementations20 Sep 2018 Bo Jiang

Comparing with traditional relaxation models, SPM can incorporate the discrete one-to-one mapping constraint straightly via a sparse constraint and thus provides a tighter relaxation for original IQP matching problem.

Structured Quasi-Newton Methods for Optimization with Orthogonality Constraints

1 code implementation3 Sep 2018 Jiang Hu, Bo Jiang, Lin Lin, Zaiwen Wen, Yaxiang Yuan

In particular, we are interested in applications that the Euclidean Hessian itself consists of a computational cheap part and a significantly expensive part.

Optimization and Control

ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection

no code implementations11 Jul 2018 Bo Jiang, Ye Liu, W. K. Chan

Decentralized cryptocurrencies feature the use of blockchain to transfer values among peers on networks without central agency.

Software Engineering Cryptography and Security

Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series

no code implementations ICML 2018 Zhengping Che, Sanjay Purushotham, Guangyu Li, Bo Jiang, Yan Liu

Multi-Rate Multivariate Time Series (MR-MTS) are the multivariate time series observations which come with various sampling rates and encode multiple temporal dependencies.

Time Series Time Series Analysis

Temporal Coherent and Graph Optimized Manifold Ranking for Visual Tracking

no code implementations17 Apr 2018 Bo Jiang, Doudou Lin, Bin Luo, Jin Tang

To address this problem, we propose a novel unified temporal coherence and graph optimized ranking model for weighted patch representation in visual tracking problem.

Graph Ranking Visual Tracking

Context-aware Data Aggregation with Localized Information Privacy

no code implementations6 Apr 2018 Bo Jiang, Ming Li, Ravi Tandon

The notion of context-awareness is incorporated in LIP by the introduction of priors, which enables the design of privacy-preserving data aggregation with knowledge of priors.

Privacy Preserving

Highly accurate model for prediction of lung nodule malignancy with CT scans

no code implementations6 Feb 2018 Jason Causey, Junyu Zhang, Shiqian Ma, Bo Jiang, Jake Qualls, David G. Politte, Fred Prior, Shuzhong Zhang, Xiuzhen Huang

Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN).

Computed Tomography (CT)

Relational Multi-Instance Learning for Concept Annotation from Medical Time Series

no code implementations ICLR 2018 Sanjay Purushotham, Zhengping Che, Bo Jiang, Tanachat Nilanon, Yan Liu

Recent advances in computing technology and sensor design have made it easier to collect longitudinal or time series data from patients, resulting in a gigantic amount of available medical data.

Time Series Time Series Analysis

Graph Matching via Multiplicative Update Algorithm

no code implementations NeurIPS 2017 Bo Jiang, Jin Tang, Chris Ding, Yihong Gong, Bin Luo

As a fundamental problem in computer vision, graph matching problem can usually be formulated as a Quadratic Programming (QP) problem with doubly stochastic and discrete (integer) constraints.

Graph Matching

Binary Constraint Preserving Graph Matching

no code implementations CVPR 2017 Bo Jiang, Jin Tang, Chris Ding, Bin Luo

There are three main contributions of the proposed method: (1) we propose a new graph matching relaxation model, called Binary Constraint Preserving Graph Matching (BPGM), which aims to incorporate the discrete binary mapping constraints more in graph matching relaxation.

Graph Matching

Revisiting L21-norm Robustness with Vector Outlier Regularization

no code implementations20 Jun 2017 Bo Jiang, Chris Ding

One interesting property of VOR is that how far an outlier lies away from its theoretically predicted value does not affect the final regularization and analysis results.

L1-norm Error Function Robustness and Outlier Regularization

no code implementations28 May 2017 Chris Ding, Bo Jiang

(1) A key property of outlier regularization is that how far an outlier lies away from its theoretically predicted value does not affect the final regularization and analysis results.

Multiple Images Recovery Using a Single Affine Transformation

no code implementations23 May 2017 Bo Jiang, Chris Ding, Bin Luo

One approach to deal with noise image data is to use data recovery techniques which aim to recover the true uncorrupted signals from the observed noise images.

Structured Nonconvex and Nonsmooth Optimization: Algorithms and Iteration Complexity Analysis

no code implementations9 May 2016 Bo Jiang, Tianyi Lin, Shiqian Ma, Shuzhong Zhang

In particular, we consider in this paper some constrained nonconvex optimization models in block decision variables, with or without coupled affine constraints.

Towards a solid solution of real-time fire and flame detection

no code implementations2 Feb 2015 Bo Jiang, Yongyi Lu, Xiying Li, Liang Lin

Although the object detection and recognition has received growing attention for decades, a robust fire and flame detection method is rarely explored.

object-detection Object Detection +1

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