no code implementations • 22 May 2024 • Vahid Jebraeeli, Bo Jiang, Derya Cansever, Hamid Krim
In the era of big data, the sheer volume and complexity of datasets pose significant challenges in machine learning, particularly in image processing tasks.
no code implementations • 13 May 2024 • Qilin Zhou, Zhengyuan Wei, Haipeng Wang, Bo Jiang, W. K. Chan
CrossCert formulates a novel approach by cross-checking two certified recovery defenders to provide unwavering certification and detection certification.
2 code implementations • 28 Apr 2024 • Ju Huang, Shiao Wang, Shuai Wang, Zhe Wu, Xiao Wang, Bo Jiang
Specifically, our Mamba-based tracker achieves 43. 5/55. 6 on the SR/PR metric, while the ViT-S based tracker (OSTrack) obtains 40. 0/50. 9.
2 code implementations • 27 Apr 2024 • Xiao Wang, Qian Zhu, Jiandong Jin, Jun Zhu, Futian Wang, Bo Jiang, YaoWei Wang, Yonghong Tian
Specifically, we formulate the video-based PAR as a vision-language fusion problem and adopt a pre-trained foundation model CLIP to extract the visual features.
1 code implementation • 27 Apr 2024 • Xiao Wang, Yuehang Li, Wentao Wu, Jiandong Jin, Yao Rong, Bo Jiang, Chuanfu Li, Jin Tang
Existing X-ray based pre-trained vision models are usually conducted on a relatively small-scale dataset (less than 500k samples) with limited resolution (e. g., 224 $\times$ 224).
no code implementations • 23 Apr 2024 • Zhengzheng Tu, Le Gu, Xixi Wang, Bo Jiang
To address these issues, in this paper, we develop a novel Breast Ultrasound SAM Adapter, termed Breast Ultrasound Segment Anything Model (BUSSAM), which migrates the SAM to the field of breast ultrasound image segmentation by using the adapter technique.
1 code implementation • 15 Apr 2024 • Xiao Wang, Shiao Wang, Yuhe Ding, Yuehang Li, Wentao Wu, Yao Rong, Weizhe Kong, Ju Huang, Shihao Li, Haoxiang Yang, Ziwen Wang, Bo Jiang, Chenglong Li, YaoWei Wang, Yonghong Tian, Jin Tang
In this paper, we give the first comprehensive review of these works and also provide experimental comparisons and analysis to better demonstrate the features and advantages of SSM.
no code implementations • 18 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.
no code implementations • 15 Mar 2024 • Qin Xu, Sitong Li, Jiahui Wang, Bo Jiang, Jinhui Tang
To tackle this challenge, we propose a weakly supervised Context-Semantic Quality Awareness Network (CSQA-Net) for FGVC.
Ranked #4 on Fine-Grained Image Classification on NABirds
Fine-Grained Image Classification Fine-Grained Visual Categorization
4 code implementations • 9 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.
no code implementations • 23 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.
1 code implementation • 20 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.
no code implementations • 8 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.
1 code implementation • 6 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".
1 code implementation • 5 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.
1 code implementation • 18 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}.
no code implementations • 12 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.
1 code implementation • 1 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.
no code implementations • 29 Nov 2023 • Bo Jiang, Jian Du, Qiang Yan
We conducted a comprehensive performance evaluation of various attack strategies proposed utilizing two real datasets.
no code implementations • 28 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.
1 code implementation • 13 Nov 2023 • Ziwei He, Jian Yuan, Le Zhou, Jingwen Leng, Bo Jiang
The quadratic complexity of self-attention in Transformers has hindered the processing of long text.
1 code implementation • 17 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.
no code implementations • 9 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.
4 code implementations • 26 Sep 2023 • Xiao Wang, Shiao Wang, Chuanming Tang, Lin Zhu, Bo Jiang, Yonghong Tian, Jin Tang
Tracking using bio-inspired event cameras has drawn more and more attention in recent years.
1 code implementation • 19 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.
1 code implementation • 5 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.
no code implementations • 15 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.
1 code implementation • 10 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.
1 code implementation • 8 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.
1 code implementation • journal 2023 • Bo Xie, Xiaohui Jia, Xiawen Song, Hua Zhang, Bi Chen, Bo Jiang, Ye Wang, Yun Pan
It usually includes slot filling and intent detection (SFID) tasks aiming at semantic parsing of utterances.
no code implementations • 3 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.
1 code implementation • 8 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.
no code implementations • 30 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.
no code implementations • 18 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.
no code implementations • 12 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.
no code implementations • 17 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.
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.
1 code implementation • 15 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.
1 code implementation • IEEE Transactions on Multimedia 2023 • Qin Xu, Jiahui Wang, Bo Jiang, Bin Luo
The proposed IELT involves three main modules: multi-head voting (MHV) module, cross-layer refinement (CLR) module, and dynamic selection (DS) module.
1 code implementation • 9 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.
no code implementations • 5 Dec 2022 • Bo Jiang, Shaoyu Chen, Xinggang Wang, Bencheng Liao, Tianheng Cheng, Jiajie Chen, Helong Zhou, Qian Zhang, Wenyu Liu, Chang Huang
Motion prediction is highly relevant to the perception of dynamic objects and static map elements in the scenarios of autonomous driving.
2 code implementations • 20 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.
Ranked #3 on Object Tracking on COESOT
no code implementations • 19 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.
2 code implementations • 17 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.
no code implementations • 2 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.
1 code implementation • 7 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.
no code implementations • 27 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.
no code implementations • 5 Sep 2022 • Beibei Wang, Bo Jiang
Graph Attention Networks (GATs) have been intensively studied and widely used in graph data learning tasks.
no code implementations • 26 Aug 2022 • Xixi Wang, Xiao Wang, Bo Jiang, Bin Luo
sampleFormer aims to capture the dependence of samples in support and query sets for image representation.
3 code implementations • 30 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.
no code implementations • 21 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.
1 code implementation • 19 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.
no code implementations • 26 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.
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)
no code implementations • 25 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.
no code implementations • 18 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.
1 code implementation • 2 Dec 2021 • Xixi Wang, Xiao Wang, Bo Jiang, Jin Tang, Bin Luo
In this work, we re-think Transformer and extend it to MutualFormer for multi-modality data representation.
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)
no code implementations • 5 Oct 2021 • Yichen Zhu, Bo Jiang, Haiming Jin, Mengtian Zhang, Feng Gao, Jianqiang Huang, Tao Lin, Xinbing Wang
An important task in such applications is to predict the future values of a NETS based on its historical values and the underlying graph.
no code implementations • 29 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.
no code implementations • 29 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.
no code implementations • 23 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.
2 code implementations • 22 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.
Ranked #29 on Rgb-T Tracking on RGBT234
no code implementations • 6 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.
no code implementations • 25 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.
2 code implementations • CVPR 2021 • Xiao Wang, Xiujun Shu, Zhipeng Zhang, Bo Jiang, YaoWei Wang, Yonghong Tian, Feng Wu
We believe this benchmark will greatly boost related researches on natural language guided tracking.
Ranked #3 on Visual Object Tracking on TNL2K (precision metric)
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.
no code implementations • 5 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.
no code implementations • 5 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.
no code implementations • 5 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.
1 code implementation • 30 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
1 code implementation • 29 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
1 code implementation • 7 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.
no code implementations • 23 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.
1 code implementation • 21 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.
no code implementations • 24 Nov 2019 • Bo Jiang, Xixi Wang, Jin Tang
Discriminative feature representation of person image is important for person re-identification (Re-ID) task.
no code implementations • 18 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.
Ranked #15 on Graph Matching on Willow Object Class
no code implementations • 4 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.
no code implementations • 4 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.
no code implementations • 14 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.
no code implementations • 20 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.
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.
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.
no code implementations • 26 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.
Ranked #29 on Node Classification on Cora
no code implementations • 3 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.
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.
no code implementations • 22 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.
no code implementations • 25 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.
no code implementations • 1 Oct 2018 • Bo Jiang, Doudou Lin, Jin Tang
We present a novel graph diffusion-embedding networks (GDEN) for graph structured data.
2 code implementations • 26 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.
no code implementations • 20 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.
1 code implementation • 3 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
no code implementations • 11 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
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.
no code implementations • 17 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.
no code implementations • 6 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.
no code implementations • 6 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).
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.
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.
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.
no code implementations • 20 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.
no code implementations • 28 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.
no code implementations • 23 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.
no code implementations • 9 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.
no code implementations • 2 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.
no code implementations • CVPR 2013 • Bo Jiang, Chris Ding, Bio Luo, Jin Tang
Principal Component Analysis (PCA) is a widely used to learn a low-dimensional representation.