104 citations · 181 across the 17 of their papers we have counts for
18 papers
CoRCi: Cross-Reconstruction of Coherent Interests Modeling in Cross-Domain Sequential Recommendation
Qingtian Bian, Tieying Li, Marcus de Carvalho +3
Cross-Domain Sequential Recommendation (CDSR) aims to alleviate data sparsity by transferring dynamic user interests across related domains. A key challenge lies in effectively bri…
Text4Seg++: Advancing Image Segmentation via Generative Language Modeling
Mengcheng Lan, Chaofeng Chen, Jiaxing Xu +6
Multimodal Large Language Models (MLLMs) have shown exceptional capabilities in vision-language tasks. However, effectively integrating image segmentation into these models remains…
NOCL: Node-Oriented Conceptualization LLM for Graph Tasks without Message Passing
Wei Li, Mengcheng Lan, Jiaxing Xu +1
Graphs are essential for modeling complex interactions across domains such as social networks, biology, and recommendation systems. Traditional Graph Neural Networks, particularly…
-GAN by Rényi Cross Entropy
Ni Ding, Miao Qiao, Jiaxing Xu +2
This paper proposes -GAN, a generative adversarial network using Rényi measures. The value function is formulated, by Rényi cross entropy, as an expected certainty measure incur…
BrainPrompt: Multi-Level Brain Prompt Enhancement for Neurological Condition Identification
Jiaxing Xu, Kai He, Yue Tang +5
Neurological conditions, such as Alzheimer's Disease, are challenging to diagnose, particularly in the early stages where symptoms closely resemble healthy controls. Existing brain…
Directed Homophily-Aware Graph Neural Network
Aihu Zhang, Jiaxing Xu, Mengcheng Lan +2
Graph Neural Networks (GNNs) have achieved significant success in various learning tasks on graph-structured data. Nevertheless, most GNNs struggle to generalize to heterophilic ne…
Divergent Paths: Separating Homophilic and Heterophilic Learning for Enhanced Graph-level Representations
Han Lei, Jiaxing Xu, Xia Dong +1
Graph Convolutional Networks (GCNs) are predominantly tailored for graphs displaying homophily, where similar nodes connect, but often fail on heterophilic graphs. The strategy of…
ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation
Qingtian Bian, Marcus Vinícius de Carvalho, Tieying Li +3
Cross-Domain Sequential Recommendation (CDSR) has recently gained attention for countering data sparsity by transferring knowledge across domains. A common approach merges domain-s…
BrainOOD: Out-of-distribution Generalizable Brain Network Analysis
Jiaxing Xu, Yongqiang Chen, Xia Dong +5
In neuroscience, identifying distinct patterns linked to neurological disorders, such as Alzheimer's and Autism, is critical for early diagnosis and effective intervention. Graph N…
Text4Seg: Reimagining Image Segmentation as Text Generation
Mengcheng Lan, Chaofeng Chen, Yue Zhou +5
Multimodal Large Language Models (MLLMs) have shown exceptional capabilities in vision-language tasks; however, effectively integrating image segmentation into these models remains…
Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion
Jiaxing Xu, Mengcheng Lan, Xia Dong +4
In the realm of neuroscience, identifying distinctive patterns associated with neurological disorders via brain networks is crucial. Resting-state functional magnetic resonance ima…
SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation
Mengcheng Lan, Xinjiang Wang, Yiping Ke +3
Unsupervised semantic segmentation is a challenging task that segments images into semantic groups without manual annotation. Prior works have primarily focused on leveraging prior…
CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning
Qingtian Bian, Jiaxing Xu, Hui Fang +1
The motivations of users to make interactions can be divided into static preference and dynamic interest. To accurately model user representations over time, recent studies in sequ…
Contrastive Graph Pooling for Explainable Classification of Brain Networks
Jiaxing Xu, Qingtian Bian, Xinhang Li +6
Functional magnetic resonance imaging (fMRI) is a commonly used technique to measure neural activation. Its application has been particularly important in identifying underlying ne…
Union Subgraph Neural Networks
Jiaxing Xu, Aihu Zhang, Qingtian Bian +2
Graph Neural Networks (GNNs) are widely used for graph representation learning in many application domains. The expressiveness of vanilla GNNs is upper-bounded by 1-dimensional Wei…
IMF: Interactive Multimodal Fusion Model for Link Prediction
Xinhang Li, Xiangyu Zhao, Jiaxing Xu +2
Link prediction aims to identify potential missing triples in knowledge graphs. To get better results, some recent studies have introduced multimodal information to link prediction…
Entity Aware Syntax Tree Based Data Augmentation for Natural Language Understanding
Jiaxing Xu, Jianbin Cui, Jiangneng Li +2
Understanding the intention of the users and recognizing the semantic entities from their sentences, aka natural language understanding (NLU), is the upstream task of many natural…
A Class-Aware Representation Refinement Framework for Graph Classification
Jiaxing Xu, Jinjie Ni, Yiping Ke
Graph Neural Networks (GNNs) are widely used for graph representation learning. Despite its prevalence, GNN suffers from two drawbacks in the graph classification task, the neglect…