210 citations · 533 across the 17 of their papers we have counts for
17 papers
GrassNet: State Space Model Meets Graph Neural Network
Gongpei Zhao, Tao Wang, Yi Jin +3
Designing spectral convolutional networks is a formidable task in graph learning. In traditional spectral graph neural networks (GNNs), polynomial-based methods are commonly used t…
DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment
Gongpei Zhao, Tao Wang, Congyan Lang +3
Graph neural networks are recognized for their strong performance across various applications, with the backpropagation algorithm playing a central role in the development of most…
The Cascaded Forward Algorithm for Neural Network Training
Gongpei Zhao, Tao Wang, Yidong Li +3
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 de…
Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges
Yushan Han, Hui Zhang, Huifang Li +3
Collaborative perception is essential to address occlusion and sensor failure issues in autonomous driving. In recent years, theoretical and experimental investigations of novel wo…
GLAN: A Graph-based Linear Assignment Network
He Liu, Tao Wang, Congyan Lang +3
Differentiable solvers for the linear assignment problem (LAP) have attracted much research attention in recent years, which are usually embedded into learning frameworks as compon…
Deep Probabilistic Graph Matching
He Liu, Tao Wang, Yidong Li +3
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 rela…
Clicking Matters:Towards Interactive Human Parsing
Yutong Gao, Liqian Liang, Congyan Lang +3
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. This new task…
MSO: Multi-Feature Space Joint Optimization Network for RGB-Infrared Person Re-Identification
Yajun Gao, Tengfei Liang, Yi Jin +4
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. Exist…
Joint Graph Learning and Matching for Semantic Feature Correspondence
He Liu, Tao Wang, Yidong Li +3
In recent years, powered by the learned discriminative representation via graph neural network (GNN) models, deep graph matching methods have made great progresses in the task of m…
A Universal Model for Cross Modality Mapping by Relational Reasoning
Zun Li, Congyan Lang, Liqian Liang +4
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. Existing method…
EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising
Tengfei Liang, Yi Jin, Yidong Li +3
In the past few decades, to reduce the risk of X-ray in computed tomography (CT), low-dose CT image denoising has attracted extensive attention from researchers, which has become a…
Cross-layer Feature Pyramid Network for Salient Object Detection
Zun Li, Congyan Lang, Junhao Liew +3
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. How…
HERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization
Gengyu Lyu, Songhe Feng, Yi Jin +3
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. Most existing m…
GM-PLL: Graph Matching based Partial Label Learning
Gengyu Lyu, Songhe Feng, Tao Wang +2
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. The key to deal…
Deep Reasoning with Multi-Scale Context for Salient Object Detection
Zun Li, Congyan Lang, Yunpeng Chen +2
To detect salient objects accurately, existing methods usually design complex backbone network architectures to learn and fuse powerful features. However, the saliency inference mo…
A Self-paced Regularization Framework for Partial-Label Learning
Gengyu Lyu, Songhe Feng, Congyang Lang
Partial label learning (PLL) aims to solve the problem where each training instance is associated with a set of candidate labels, one of which is the correct label. Most PLL algori…
Multiple-Human Parsing in the Wild
Jianshu Li, Jian Zhao, Yunchao Wei +5
Human parsing is attracting increasing research attention. In this work, we aim to push the frontier of human parsing by introducing the problem of multi-human parsing in the wild.…