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Found 85 papers, 32 papers with code
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.
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.
Semantic-Aware Frame-Event Fusion based Pattern Recognition via Large Vision-Language Models
Current methods typically employ backbone networks to individually extract the features of RGB frames and event streams, and subsequently fuse these features for pattern recognition.
Robust Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation
Label propagation methods try to propagate the labels of support samples on the constructed graph encoding the relationships between both support and query samples.
Unified-modal Salient Object Detection via Adaptive Prompt Learning
Existing single-modal and multi-modal salient object detection (SOD) methods focus on designing specific architectures tailored for their respective tasks.
WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models
This paper introduces WordArt Designer, a user-driven framework for artistic typography synthesis, relying on the Large Language Model (LLM).
VcT: Visual change Transformer for Remote Sensing Image Change Detection
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.
DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs
To address this, we propose a novel architecture called Darkness Clue-Prompted Tracking (DCPT) that achieves robust UAV tracking at night by efficiently learning to generate darkness clue prompts.
Refined Temporal Pyramidal Compression-and-Amplification Transformer for 3D Human Pose Estimation
Accurately estimating the 3D pose of humans in video sequences requires both accuracy and a well-structured architecture.
PoSynDA: Multi-Hypothesis Pose Synthesis Domain Adaptation for Robust 3D Human Pose Estimation
Typically, PoSynDA uses a diffusion-inspired structure to simulate 3D pose distribution in the target domain.
Towards Deeply Unified Depth-aware Panoptic Segmentation with Bi-directional Guidance Learning
Our method sets the new state of the art for depth-aware panoptic segmentation on both Cityscapes-DVPS and SemKITTI-DVPS datasets.
Tracking Anything in High Quality
To further improve the quality of tracking masks, a pretrained MR model is employed to refine the tracking results.
Ranked #5 on
Semi-Supervised Video Object Segmentation
on YouTube-VOS 2019
(using extra training data)
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+2
Semantic-Aware Dual Contrastive Learning for Multi-label Image Classification
Specifically, we leverage semantic-aware representation learning to extract category-related local discriminative features and construct category prototypes.
Ranked #1 on
Multi-Label Learning
on COCO 2014
Sparse-Input Neural Network using Group Concave Regularization
To overcome this limitation, we propose a framework of sparse-input neural networks using group concave regularization for feature selection in both low-dimensional and high-dimensional settings.
Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving
While federated learning (FL) improves the generalization of end-to-end autonomous driving by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence rate due to long-range communications among vehicles and cloud server.
KeyPosS: Plug-and-Play Facial Landmark Detection through GPS-Inspired True-Range Multilateration
By spearheading the integration of Multilateration with facial analysis, KeyPosS marks a paradigm shift in facial landmark detection.
Overcoming Topology Agnosticism: Enhancing Skeleton-Based Action Recognition through Redefined Skeletal Topology Awareness
This finding exposes the limitations of a single adjacency matrix in capturing the variations of relational configurations emblematic of human movement, which we remedy by proposing an efficient refinement to Graph Convolutions (GC) - the BlockGC.
Adversarial Examples Detection with Enhanced Image Difference Features based on Local Histogram Equalization
However, numerous studies have proven that previous methods create detection or defense against certain attacks, which renders the method ineffective in the face of the latest unknown attack methods.
DAMO-StreamNet: Optimizing Streaming Perception in Autonomous Driving
Real-time perception, or streaming perception, is a crucial aspect of autonomous driving that has yet to be thoroughly explored in existing research.
Judicial Intelligent Assistant System: Extracting Events from Divorce Cases to Detect Disputes for the Judge
It is a difficult but necessary task to extract the key information for the cases from these textual materials and to clarify the dispute focus of related parties.
Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection
At each MIL training iteration, we use the current detector to divide the samples into two groups with different context biases: the most confident abnormal/normal snippets and the rest ambiguous ones.
Fine-Grained Visual Classification via Internal Ensemble Learning Transformer
The proposed IELT involves three main modules: multi-head voting (MHV) module, cross-layer refinement (CLR) module, and dynamic selection (DS) module.
CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models
Despite the recent advances showing that a model pre-trained on large-scale source code data is able to gain appreciable generalization capability, it still requires a sizeable amount of data on the target task for fine-tuning.
HDFormer: High-order Directed Transformer for 3D Human Pose Estimation
Human pose estimation is a challenging task due to its structured data sequence nature.
Ranked #73 on
3D Human Pose Estimation
on Human3.6M
Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning
However, the relation between these two losses is not clear.
Adversarial Example Defense via Perturbation Grading Strategy
In addition, the proposed method does not modify any task model, which can be used as a preprocessing module, which significantly reduces the deployment cost in practical applications.
Rethinking Batch Sample Relationships for Data Representation: A Batch-Graph Transformer based Approach
(1) It employs a flexible graph model, termed Batch Graph to jointly encode the visual and semantic relationships of samples within each mini-batch.
Data Dimension Reduction makes ML Algorithms efficient
Similarly, in AE based DDR, we compare unsupervised learning algorithm accuracy and time before and after AE representation learning.
ProContEXT: Exploring Progressive Context Transformer for Tracking
Existing Visual Object Tracking (VOT) only takes the target area in the first frame as a template.
LongShortNet: Exploring Temporal and Semantic Features Fusion in Streaming Perception
Streaming perception is a critical task in autonomous driving that requires balancing the latency and accuracy of the autopilot system.
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding
Recent years have witnessed the rise and success of pre-training techniques in visually-rich document understanding.
Ranked #2 on
Semantic entity labeling
on FUNSD
Cost-effective photonic super-resolution millimeter-wave joint radar-communication system using self-coherent detection
Then the self-coherent detection, as a simple and low-cost means, is accordingly facilitated for both de-chirping of MMW radar and frequency down-conversion reception of MMW communication, which circumvents the costly high-speed mixers along with MMW local oscillators and more significantly achieves the real-time decomposition of radar and communication information.
ERNIE-mmLayout: Multi-grained MultiModal Transformer for Document Understanding
At first, a document graph is proposed to model complex relationships among multi-grained multimodal elements, in which salient visual regions are detected by a cluster-based method.
SQL and NoSQL Databases Software architectures performance analysis and assessments -- A Systematic Literature review
Also, the mixed model is followed by each category of NoSQL Databases.
Few-Shot Learning Meets Transformer: Unified Query-Support Transformers for Few-Shot Classification
sampleFormer aims to capture the dependence of samples in support and query sets for image representation.
An Extractive-and-Abstractive Framework for Source Code Summarization
The extractive module in the framework performs a task of extractive code summarization, which takes in the code snippet and predicts important statements containing key factual details.
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
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.
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.
Neural Program Repair: Systems, Challenges and Solutions
Automated Program Repair (APR) aims to automatically fix bugs in the source code.
Universal adversarial perturbation for remote sensing images
Firstly, the former is used to generate the UAP, which can learn the distribution of perturbations better, and then the latter is used to find the sensitive regions concerned by the RSI classification model.
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.
AST-Transformer: Encoding Abstract Syntax Trees Efficiently for Code Summarization
Code summarization aims to generate brief natural language descriptions for source code.
MutualFormer: Multi-Modality Representation Learning via Cross-Diffusion Attention
In this work, we re-think Transformer and extend it to MutualFormer for multi-modality data representation.
GAMnet: Robust Feature Matching via Graph Adversarial-Matching Network
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 #4 on
Graph Matching
on PASCAL VOC
(matching accuracy metric)
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.
Ranked #13 on
Graph Matching
on Willow Object Class
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 #28 on
Node Classification
on Cora
Pedestrian Attribute Recognition: A Survey
We also review some popular network architectures which have been widely applied in the deep learning community.
Multiple Graph Adversarial Learning
In this paper, we propose a novel Multiple Graph Adversarial Learning (MGAL) framework for multi-graph representation and learning.
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.
