no code implementations • Findings (EMNLP) 2021 • Yi Feng, Ting Wang, Chuanyi Li, Vincent Ng, Jidong Ge, Bin Luo, Yucheng Hu, Xiaopeng Zhang
User targeting is an essential task in the modern advertising industry: given a package of ads for a particular category of products (e. g., green tea), identify the online users to whom the ad package should be targeted.
no code implementations • EMNLP 2020 • Li Kong, Chuanyi Li, Jidong Ge, Bin Luo, Vincent Ng
While hyperbole is one of the most prevalent rhetorical devices, it is arguably one of the least studied devices in the figurative language processing community.
no code implementations • 24 May 2022 • Changan Niu, Chuanyi Li, Bin Luo, Vincent Ng
In particular, the development and use of pre-trained models of source code has enabled state-of-the-art results to be achieved on a wide variety of SE tasks.
no code implementations • 19 May 2022 • Xiao Wang, Zhe Chen, Jin Tang, Bin Luo, DaCheng Tao
Existing trackers usually select a location or proposal with the maximum score as tracking result for each frame.
no code implementations • 10 May 2022 • Jingxiao Liu, Siyuan Yuan, Bin Luo, Biondo Biondi, Hae Young Noh
Bridge Health Monitoring (BHM) enables early damage detection of bridges and is thus critical for avoiding more severe damages that might result in major financial and human losses.
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 • 22 Feb 2022 • Zhaoxia Yin, Qingyu Wang, Jin Tang, Bin Luo
Recently, with the application of deep learning in the remote sensing image (RSI) field, the classification accuracy of the RSI has been greatly improved compared with traditional technology.
no code implementations • 22 Feb 2022 • Wenkang Zhong, Chuanyi Li, Jidong Ge, Bin Luo
Automated Program Repair (APR) aims to automatically fix bugs in the source code.
1 code implementation • 11 Feb 2022 • Yabin Zhu, Chenglong Li, Yao Liu, Xiao Wang, Jin Tang, Bin Luo, Zhixiang Huang
Tiny objects, frequently appearing in practical applications, have weak appearance and features, and receive increasing interests in meany vision tasks, such as object detection and segmentation.
1 code implementation • 13 Dec 2021 • Yunyun huang, Xiaoyu Shen, Chuanyi Li, Jidong Ge, Bin Luo
Given the fact of a case, Legal Judgment Prediction (LJP) involves a series of sub-tasks such as predicting violated law articles, charges and term of penalty.
no code implementations • 2 Dec 2021 • Xixi Wang, Bo Jiang, Xiao Wang, Bin Luo
In this work, we re-thinking the self-attention based Transformer and propose a novel MutualFormer for multi-modality data fusion and representation.
no code implementations • 2 Dec 2021 • Ze Tang, Chuanyi Li, Jidong Ge, Xiaoyu Shen, Zheling Zhu, Bin Luo
Code summarization aims to generate brief natural language descriptions for source code.
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 • 4 Sep 2021 • Chenjie Wang, Chengyuan Li, Bin Luo, Wei Wang, Jun Liu
Then we extend SOLOV2 to capture temporal information in video to learn motion information, and propose a moving object instance segmentation network with RiWFPN called RiWNet.
1 code implementation • 9 Jun 2021 • Xiao Wang, Jin Tang, Bin Luo, YaoWei Wang, Yonghong Tian, Feng Wu
In this paper, we propose a novel and general target-aware attention mechanism (termed TANet) and integrate it with tracking-by-detection framework to conduct joint local and global search for robust tracking.
1 code implementation • 27 Apr 2021 • Chenglong Li, Wanlin Xue, Yaqing Jia, Zhichen Qu, Bin Luo, Jin Tang, Dengdi Sun
RGBT tracking receives a surge of interest in the computer vision community, but this research field lacks a large-scale and high-diversity benchmark dataset, which is essential for both the training of deep RGBT trackers and the comprehensive evaluation of RGBT tracking methods.
1 code implementation • 30 Mar 2021 • Xiao Wang, Zhe Chen, Jin Tang, Bin Luo, YaoWei Wang, Yonghong Tian, Feng Wu
In this paper, we propose to introduce more dynamics by devising a dynamic attention-guided multi-trajectory tracking strategy.
no code implementations • 19 Jan 2021 • Jie Wang, Zhaoxia Yin, Jin Tang, Jing Jiang, Bin Luo
The studies on black-box adversarial attacks have become increasingly prevalent due to the intractable acquisition of the structural knowledge of deep neural networks (DNNs).
no code implementations • 18 Dec 2020 • Chengyuan Li, Jun Liu, Hailong Hong, Wenju Mao, Chenjie Wang, Chudi Hu, Xin Su, Bin Luo
On the basis of this, a novel octave convolution-based semantic attention feature pyramid network (OcSaFPN) is proposed to get higher accuracy in object detection with noise.
no code implementations • 14 Nov 2020 • Andong Lu, Chenglong Li, Yuqing Yan, Jin Tang, Bin Luo
In specific, we use the modified VGG-M as the generality adapter to extract the modality-shared target representations. To extract the modality-specific features while reducing the computational complexity, we design a modality adapter, which adds a small block to the generality adapter in each layer and each modality in a parallel manner.
no code implementations • 26 Jul 2020 • Chenjie Wang, Chengyuan Li, Bin Luo
Most scenes in practical applications are dynamic scenes containing moving objects, so segmenting accurately moving objects is crucial for many computer vision applications.
no code implementations • 9 Jun 2020 • Weixing Liu, Jun Liu, Bin Luo
Deep learning approaches require enough training samples to perform well, but it is a challenge to collect enough real training data and label them manually.
no code implementations • 17 Mar 2020 • Zhengzheng Tu, Chun Lin, Chenglong Li, Jin Tang, Bin Luo
Classifying the confusing samples in the course of RGBT tracking is a quite challenging problem, which hasn't got satisfied solution.
no code implementations • 10 Mar 2020 • Chenjie Wang, Bin Luo, Yun Zhang, Qing Zhao, Lu Yin, Wei Wang, Xin Su, Yajun Wang, Chengyuan Li
The only input of DymSLAM is stereo video, and its output includes a dense map of the static environment, 3D model of the moving objects and the trajectories of the camera and the moving objects.
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 • 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.
no code implementations • 15 Oct 2019 • Qing Zhao, Bin Luo, Yun Zhang
In this letter, a stereo-based multi-motion visual odometry method is proposed to acquire the poses of the robot and other moving objects.
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 • 24 Aug 2019 • Joya Chen, Dong Liu, Bin Luo, Xuezheng Peng, Tong Xu, Enhong Chen
For a long time, object detectors have suffered from extreme imbalance between foregrounds and backgrounds.
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 • 7 Aug 2019 • Zhengzheng Tu, Yan Ma, Chenglong Li, Jin Tang, Bin Luo
To maintain the clear edge structure of salient objects, we propose a novel Edge-guided Non-local FCN (ENFNet) to perform edge guided feature learning for accurate salient object detection.
no code implementations • 24 Jul 2019 • Yabin Zhu, Chenglong Li, Bin Luo, Jin Tang, Xiao Wang
In different modalities, we propose to prune the densely aggregated features of all modalities in a collaborative way.
1 code implementation • 24 Jul 2019 • Chenglong Li, Wei Xia, Yan Yan, Bin Luo, Jin Tang
These advantages of thermal infrared cameras make the segmentation of semantic objects in day and night.
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 • 22 May 2019 • Hongchao Li, Xianmin Lin, Aihua Zheng, Chenglong Li, Bin Luo, Ran He, Amir Hussain
In particular, our network is end-to-end trained and contains three subnetworks of deep features embedded by the corresponding attributes (i. e., camera view, vehicle type and vehicle color).
no code implementations • 6 May 2019 • Xiao Wang, Ziliang Chen, Rui Yang, Bin Luo, Jin Tang
In this paper, we propose Hard Person Identity Mining (HPIM) that attempts to refine the hard example mining to improve the exploration efficacy in person re-identification.
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 #25 on
Node Classification
on Cora
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 • 22 Jan 2019 • Xiao Wang, Shaofei Zheng, Rui Yang, Bin Luo, Jin Tang
We also review some popular network architectures which have widely applied in the deep learning community.
no code implementations • 27 Nov 2018 • Xiao Wang, Tao Sun, Rui Yang, Chenglong Li, Bin Luo, Jin Tang
In this paper, we propose an efficient quality-aware deep neural network to model the weight of data from each domain using deep reinforcement learning (DRL).
no code implementations • 25 Nov 2018 • Xiao Wang, Chenglong Li, Rui Yang, Tianzhu Zhang, Jin Tang, Bin Luo
To refine the states of the target and re-track the target when it is back to view from heavy occlusion and out of view, we elaborately design a novel subnetwork to learn the target-driven visual attentions from the guidance of both visual and natural language cues.
no code implementations • 24 Nov 2018 • Yabin Zhu, Chenglong Li, Bin Luo, Jin Tang
This paper investigates how to perform robust visual tracking in adverse and challenging conditions using complementary visual and thermal infrared data (RGBT tracking).
no code implementations • CVPR 2018 • Xiao Wang, Chenglong Li, Bin Luo, Jin Tang
Based on the generated hard positive samples, we train a Siamese network for visual tracking and our experiments validate the effectiveness of the introduced algorithm.
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 • 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 • 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 • 11 Jan 2017 • Chenglong Li, Guizhao Wang, Yunpeng Ma, Aihua Zheng, Bin Luo, Jin Tang
In particular, we introduce a weight for each modality to describe the reliability, and integrate them into the graph-based manifold ranking algorithm to achieve adaptive fusion of different source data.