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Found 32 papers, 9 papers with code
GrassNet: State Space Model Meets Graph Neural Network
To overcome these issues, in this paper, we propose Graph State Space Network (GrassNet), a novel graph neural network with theoretical support that provides a simple yet effective scheme for designing and learning arbitrary graph spectral filters.
Beyond Similarity: Personalized Federated Recommendation with Composite Aggregation
Current methods mainly leverage aggregation functions invented by federated vision community to aggregate parameters from similar clients, e. g., clustering aggregation.
DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment
Specifically, DFA-GNN extends the principles of DFA to adapt to graph data and unique architecture of GNNs, which incorporates the information of graph topology into the feedback links to accommodate the non-Euclidean characteristics of graph data.
TransFR: Transferable Federated Recommendation with Pre-trained Language Models
To this end, we propose a transferable federated recommendation model with universal textual representations, TransFR, which delicately incorporates the general capabilities empowered by pre-trained language models and the personalized abilities by fine-tuning local private data.
Bridging the Gap: Multi-Level Cross-Modality Joint Alignment for Visible-Infrared Person Re-Identification
Visible-Infrared person Re-IDentification (VI-ReID) is a challenging cross-modality image retrieval task that aims to match pedestrians' images across visible and infrared cameras.
Cross-Modality Person Re-identification
Image Classification
+4
SSC3OD: Sparsely Supervised Collaborative 3D Object Detection from LiDAR Point Clouds
Extensive experiments on three large-scale datasets reveal that our proposed SSC3OD can effectively improve the performance of sparsely supervised collaborative 3D object detectors.
The Cascaded Forward Algorithm for Neural Network Training
Backpropagation algorithm has been widely used as a mainstream learning procedure for neural networks in the past decade, and has played a significant role in the development of deep learning.
Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges
The former focuses on collaboration modules and efficiency, and the latter is devoted to addressing the problems in actual application.
Transferable Adversarial Attack for Both Vision Transformers and Convolutional Networks via Momentum Integrated Gradients
Visual Transformers (ViTs) and Convolutional Neural Networks (CNNs) are the two primary backbone structures extensively used in various vision tasks.
LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization
Federated recommender system (FRS), which enables many local devices to train a shared model jointly without transmitting local raw data, has become a prevalent recommendation paradigm with privacy-preserving advantages.
Boundary Corrected Multi-scale Fusion Network for Real-time Semantic Segmentation
Image semantic segmentation aims at the pixel-level classification of images, which has requirements for both accuracy and speed in practical application.
LighTN: Light-weight Transformer Network for Performance-overhead Tradeoff in Point Cloud Downsampling
Compared with traditional task-irrelevant downsampling methods, task-oriented neural networks have shown improved performance in point cloud downsampling range.
Deep Probabilistic Graph Matching
Most previous learning-based graph matching algorithms solve the \textit{quadratic assignment problem} (QAP) by dropping one or more of the matching constraints and adopting a relaxed assignment solver to obtain sub-optimal correspondences.
GLAN: A Graph-based Linear Assignment Network
The experimental results on a synthetic dataset reveal that our method outperforms state-of-the-art baselines and achieves consistently high accuracy with the increment of the problem size.
Camera-aware Style Separation and Contrastive Learning for Unsupervised Person Re-identification
Unsupervised person re-identification (ReID) is a challenging task without data annotation to guide discriminative learning.
Clicking Matters:Towards Interactive Human Parsing
In this work, we focus on Interactive Human Parsing (IHP), which aims to segment a human image into multiple human body parts with guidance from users' interactions.
MSO: Multi-Feature Space Joint Optimization Network for RGB-Infrared Person Re-Identification
The RGB-infrared cross-modality person re-identification (ReID) task aims to recognize the images of the same identity between the visible modality and the infrared modality.
Cross-Modality Person Re-identification
Person Re-Identification
CMTR: Cross-modality Transformer for Visible-infrared Person Re-identification
The existing convolutional neural network-based methods mainly face the problem of insufficient perception of modalities' information, and can not learn good discriminative modality-invariant embeddings for identities, which limits their performance.
Cross-Modality Person Re-identification
Person Re-Identification
Joint Graph Learning and Matching for Semantic Feature Correspondence
In this paper, we propose a joint \emph{graph learning and matching} network, named GLAM, to explore reliable graph structures for boosting graph matching.
A Universal Model for Cross Modality Mapping by Relational Reasoning
With the aim of matching a pair of instances from two different modalities, cross modality mapping has attracted growing attention in the computer vision community.
Attention Models for Point Clouds in Deep Learning: A Survey
Recently, the advancement of 3D point clouds in deep learning has attracted intensive research in different application domains such as computer vision and robotic tasks.
Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline
To address these issues, in this work we carefully design our settings and propose a new dataset including both synthetic and real traffic data in more complex scenarios.
FASG: Feature Aggregation Self-training GCN for Semi-supervised Node Classification
Recently, Graph Convolutioal Networks (GCNs) have achieved significant success in many graph-based learning tasks, especially for node classification, due to its excellent ability in representation learning.
Robust Data Hiding Using Inverse Gradient Attention
In deep data hiding models, to maximize the encoding capacity, each pixel of the cover image ought to be treated differently since they have different sensitivities w. r. t.
EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising
In this paper, we propose the Edge enhancement based Densely connected Convolutional Neural Network (EDCNN).
Ranked #1 on
Denoising
on AAPM
Cross-layer Feature Pyramid Network for Salient Object Detection
Feature pyramid network (FPN) based models, which fuse the semantics and salient details in a progressive manner, have been proven highly effective in salient object detection.
HERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization
Partial Label Learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct.
Attention-based Supply-Demand Prediction for Autonomous Vehicles
Short term temporal dependency is captured with LSTM.
Domain Adaptive Attention Learning for Unsupervised Person Re-Identification
In this approach, a domain adaptive attention model is learned to separate the feature map into domain-shared part and domain-specific part.
GM-PLL: Graph Matching based Partial Label Learning
Partial Label Learning (PLL) aims to learn from the data where each training example is associated with a set of candidate labels, among which only one is correct.
Multiple-Human Parsing in the Wild
To address the multi-human parsing problem, we introduce a new multi-human parsing (MHP) dataset and a novel multi-human parsing model named MH-Parser.
Ranked #3 on
Multi-Human Parsing
on MHP v1.0
