358 citations · 1.1k across the 59 of their papers we have counts for
18 papers · 1 filter
TopoImb: Toward Topology-level Imbalance in Learning from Graphs
Tianxiang Zhao, Dongsheng Luo, Xiang Zhang +1
Graph serves as a powerful tool for modeling data that has an underlying structure in non-Euclidean space, by encoding relations as edges and entities as nodes. Despite development…
Learning to See Through with Events
Lei Yu, Xiang Zhang, Wei Liao +2
Although synthetic aperture imaging (SAI) can achieve the seeing-through effect by blurring out off-focus foreground occlusions while recovering in-focus occluded scenes from multi…
Personalized Federated Learning via Heterogeneous Modular Networks
Tianchun Wang, Wei Cheng, Dongsheng Luo +5
Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention. Despite…
On codimension one partially hyperbolic diffeomorphisms
Xiang Zhang
We show that every codimension one partially hyperbolic diffeomorphism must support on . It is locally uniquely integrable and derived from a linear codimension one…
FIXED: Frustratingly Easy Domain Generalization with Mixup
Wang Lu, Jindong Wang, Han Yu +4
Domain generalization (DG) aims to learn a generalizable model from multiple training domains such that it can perform well on unseen target domains. A popular strategy is to augme…
HP-GMN: Graph Memory Networks for Heterophilous Graphs
Junjie Xu, Enyan Dai, Xiang Zhang +1
Graph neural networks (GNNs) have achieved great success in various graph problems. However, most GNNs are Message Passing Neural Networks (MPNNs) based on the homophily assumption…
Multimodal Channel-Mixing: Channel and Spatial Masked AutoEncoder on Facial Action Unit Detection
Xiang Zhang, Huiyuan Yang, Taoyue Wang +2
Recent studies have focused on utilizing multi-modal data to develop robust models for facial Action Unit (AU) detection. However, the heterogeneity of multi-modal data poses chall…
Link Prediction on Heterophilic Graphs via Disentangled Representation Learning
Shijie Zhou, Zhimeng Guo, Charu Aggarwal +2
Link prediction is an important task that has wide applications in various domains. However, the majority of existing link prediction approaches assume the given graph follows homo…
Synthetic Over-sampling for Imbalanced Node Classification with Graph Neural Networks
Tianxiang Zhao, Xiang Zhang, Suhang Wang
In recent years, graph neural networks (GNNs) have achieved state-of-the-art performance for node classification. However, most existing GNNs would suffer from the graph imbalance…
Towards Faithful and Consistent Explanations for Graph Neural Networks
Tianxiang Zhao, Dongsheng Luo, Xiang Zhang +1
Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critic…
Unifying Motion Deblurring and Frame Interpolation with Events
Xiang Zhang, Lei Yu
Slow shutter speed and long exposure time of frame-based cameras often cause visual blur and loss of inter-frame information, degenerating the overall quality of captured videos. T…
On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation
Wenju Zhang, Xiang Zhang, Qing Liao +5
Nuclear norm maximization has shown the power to enhance the transferability of unsupervised domain adaptation model (UDA) in an empirical scheme. In this paper, we identify a new…
Learning to Purification for Unsupervised Person Re-identification
Long Lan, Xiao Teng, Jing Zhang +2
Unsupervised person re-identification is a challenging and promising task in computer vision. Nowadays unsupervised person re-identification methods have achieved great progress by…
Knowledge-Spreader: Learning Facial Action Unit Dynamics with Extremely Limited Labels
Xiaotian Li, Xiang Zhang, Taoyue Wang +1
Recent studies on the automatic detection of facial action unit (AU) have extensively relied on large-sized annotations. However, manually AU labeling is difficult, time-consuming,…
Multi-Modal Learning for AU Detection Based on Multi-Head Fused Transformers
Xiang Zhang, Lijun Yin
Multi-modal learning has been intensified in recent years, especially for applications in facial analysis and action unit detection whilst there still exist two main challenges in…
New criterions on nonexistence of periodic orbits of planar dynamical systems and their applications
Hebai Chen, Hao Yang, Rui Zhang +1
Characterizing existence or not of periodic orbit is a classical problem and it has both theoretical importance and many real applications. Here, several new criterions on nonexist…
Exploring Edge Disentanglement for Node Classification
Tianxiang Zhao, Xiang Zhang, Suhang Wang
Edges in real-world graphs are typically formed by a variety of factors and carry diverse relation semantics. For example, connections in a social network could indicate friendship…
AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models
Dongkuan Xu, Subhabrata Mukherjee, Xiaodong Liu +5
Knowledge distillation (KD) methods compress large models into smaller students with manually-designed student architectures given pre-specified computational cost. This requires s…