Search Results for author:
Found 49 papers, 7 papers with code
Don’t Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting
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
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
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
Existing trackers usually select a location or proposal with the maximum score as tracking result for each frame.
Vibration-Based Bridge Health Monitoring using Telecommunication Cables
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
Graph Convolutional Networks (GCNs) have been widely demonstrated their powerful ability in graph data representation and learning.
Universal adversarial perturbation for remote sensing images
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.
Neural Program Repair: Systems, Challenges and Solutions
Automated Program Repair (APR) aims to automatically fix bugs in the source code.
Tiny Object Tracking: A Large-scale Dataset and A Baseline
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.
Dependency Learning for Legal Judgment Prediction with a Unified Text-to-Text Transformer
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
In this work, we re-thinking the self-attention based Transformer and propose a novel MutualFormer for multi-modality data fusion and representation.
AST-Transformer: Encoding Abstract Syntax Trees Efficiently for Code Summarization
Code summarization aims to generate brief natural language descriptions for source code.
Robust Graph Data Learning with Latent Graph Convolutional Representation
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.
RiWNet: A moving object instance segmentation Network being Robust in adverse Weather conditions
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.
Tracking by Joint Local and Global Search: A Target-aware Attention based Approach
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.
LasHeR: A Large-scale High-diversity Benchmark for RGBT Tracking
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.
Dynamic Attention guided Multi-Trajectory Analysis for Single Object Tracking
In this paper, we propose to introduce more dynamics by devising a dynamic attention-guided multi-trajectory tracking strategy.
PICA: A Pixel Correlation-based Attentional Black-box Adversarial Attack
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).
Object Detection based on OcSaFPN in Aerial Images with Noise
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.
RGBT Tracking via Multi-Adapter Network with Hierarchical Divergence Loss
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.
U2-ONet: A Two-level Nested Octave U-structure with Multiscale Attention Mechanism for Moving Instances Segmentation
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?
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.
M$^5$L: Multi-Modal Multi-Margin Metric Learning for RGBT Tracking
Classifying the confusing samples in the course of RGBT tracking is a quite challenging problem, which hasn't got satisfied solution.
DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation
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.
\emph{cm}SalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks
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.
GLMNet: Graph Learning-Matching Networks for Feature Matching
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.
Stereo-based Multi-motion Visual Odometry for Mobile Robots
In this letter, a stereo-based multi-motion visual odometry method is proposed to acquire the poses of the robot and other moving objects.
Context-Aware Graph Attention Networks
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.
GmCN: Graph Mask Convolutional Network
Graph Convolutional Networks (GCNs) have shown very powerful for graph data representation and learning tasks.
Residual Objectness for Imbalance Reduction
For a long time, object detectors have suffered from extreme imbalance between foregrounds and backgrounds.
Semi-supervised Learning with Adaptive Neighborhood Graph Propagation Network
In this paper, we first re-interpret graph convolution operation in GCNs as a composition of feature propagation and (non-linear) transformation.
Edge-guided Non-local Fully Convolutional Network for Salient Object Detection
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.
Dense Feature Aggregation and Pruning for RGBT Tracking
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
These advantages of thermal infrared cameras make the segmentation of semantic objects in day and night.
PH-GCN: Person Re-identification with Part-based Hierarchical Graph Convolutional Network
Given a person image, PH-GCN first constructs a hierarchical graph to represent the pairwise relationships among different parts.
Attributes Guided Feature Learning for Vehicle Re-identification
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).
Improved Hard Example Mining by Discovering Attribute-based Hard Person Identity
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.
Robust Graph Data Learning via Latent Graph Convolutional Representation
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
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on Cora
Multiple Graph Adversarial Learning
In this paper, we propose a novel Multiple Graph Adversarial Learning (MGAL) framework for multi-graph representation and learning.
Pedestrian Attribute Recognition: A Survey
We also review some popular network architectures which have widely applied in the deep learning community.
Quality-Aware Multimodal Saliency Detection via Deep Reinforcement Learning
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).
Describe and Attend to Track: Learning Natural Language guided Structural Representation and Visual Attention for Object Tracking
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.
FANet: Quality-Aware Feature Aggregation Network for Robust RGB-T Tracking
This paper investigates how to perform robust visual tracking in adverse and challenging conditions using complementary visual and thermal infrared data (RGBT tracking).
SINT++: Robust Visual Tracking via Adversarial Positive Instance Generation
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.
Temporal Coherent and Graph Optimized Manifold Ranking for Visual Tracking
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 Matching via Multiplicative Update Algorithm
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.
Binary Constraint Preserving Graph Matching
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.
Multiple Images Recovery Using a Single Affine Transformation
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
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.
