Search Results for author: Bin Luo

Found 49 papers, 7 papers with code

Don’t Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting

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.

Identifying Exaggerated Language

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.

Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code

no code implementations24 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.

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

no code implementations19 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.

Decision Making Frame +2

Vibration-Based Bridge Health Monitoring using Telecommunication Cables

no code implementations10 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.

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.

Universal adversarial perturbation for remote sensing images

no code implementations22 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.

Classification Object Recognition

Neural Program Repair: Systems, Challenges and Solutions

no code implementations22 Feb 2022 Wenkang Zhong, Chuanyi Li, Jidong Ge, Bin Luo

Automated Program Repair (APR) aims to automatically fix bugs in the source code.

Program Repair

Tiny Object Tracking: A Large-scale Dataset and A Baseline

1 code implementation11 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.

Frame Knowledge Distillation +2

Dependency Learning for Legal Judgment Prediction with a Unified Text-to-Text Transformer

1 code implementation13 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.

MutualFormer: Multi-Modality Representation Learning via Mutual Transformer

no code implementations2 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.

Representation Learning RGB-D Salient Object Detection +2

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

RiWNet: A moving object instance segmentation Network being Robust in adverse Weather conditions

no code implementations4 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.

Instance Segmentation Semantic Segmentation

Tracking by Joint Local and Global Search: A Target-aware Attention based Approach

1 code implementation9 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.

Frame Object Tracking

LasHeR: A Large-scale High-diversity Benchmark for RGBT Tracking

1 code implementation27 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.

Frame

Dynamic Attention guided Multi-Trajectory Analysis for Single Object Tracking

1 code implementation30 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.

Frame Object Tracking

PICA: A Pixel Correlation-based Attentional Black-box Adversarial Attack

no code implementations19 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).

Adversarial Attack

Object Detection based on OcSaFPN in Aerial Images with Noise

no code implementations18 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.

Denoising Object Detection

RGBT Tracking via Multi-Adapter Network with Hierarchical Divergence Loss

no code implementations14 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.

Representation Learning Visual Tracking

U2-ONet: A Two-level Nested Octave U-structure with Multiscale Attention Mechanism for Moving Instances Segmentation

no code implementations26 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.

Can Synthetic Data Improve Object Detection Results for Remote Sensing Images?

no code implementations9 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.

Object Detection

M$^5$L: Multi-Modal Multi-Margin Metric Learning for RGBT Tracking

no code implementations17 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.

Metric Learning

DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation

no code implementations10 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.

Motion Segmentation

\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 RGB-D Salient Object Detection +3

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

Stereo-based Multi-motion Visual Odometry for Mobile Robots

no code implementations15 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.

Motion Segmentation Visual Odometry

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

Residual Objectness for Imbalance Reduction

no code implementations24 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.

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

Edge-guided Non-local Fully Convolutional Network for Salient Object Detection

no code implementations7 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.

RGB Salient Object Detection Salient Object Detection

Dense Feature Aggregation and Pruning for RGBT Tracking

no code implementations24 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.

Segmenting Objects in Day and Night:Edge-Conditioned CNN for Thermal Image Semantic Segmentation

1 code implementation24 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.

Semantic Segmentation

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

Attributes Guided Feature Learning for Vehicle Re-identification

no code implementations22 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).

Vehicle Re-Identification

Improved Hard Example Mining by Discovering Attribute-based Hard Person Identity

no code implementations6 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.

Metric Learning Person Re-Identification

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

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.

Pedestrian Attribute Recognition: A Survey

no code implementations22 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.

Multi-Label Learning Multi-Task Learning +1

Quality-Aware Multimodal Saliency Detection via Deep Reinforcement Learning

no code implementations27 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).

Decision Making reinforcement-learning +3

Describe and Attend to Track: Learning Natural Language guided Structural Representation and Visual Attention for Object Tracking

no code implementations25 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.

Object Tracking

FANet: Quality-Aware Feature Aggregation Network for Robust RGB-T Tracking

no code implementations24 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).

Rgb-T Tracking

SINT++: Robust Visual Tracking via Adversarial Positive Instance Generation

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.

Visual Tracking

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.

Frame Graph Ranking +1

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

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.

A Unified RGB-T Saliency Detection Benchmark: Dataset, Baselines, Analysis and A Novel Approach

no code implementations11 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.

Saliency Detection

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