26 citations · 45 across the 16 of their papers we have counts for
18 papers
AOI-Net: Structural Face AOI-Guided Eye-Gaze Track Representation Learning for Autism Spectrum Disorder Detection
Zhanpei Huang, Binbin Sun, Jialiang Chen +5
Eye-movement tracking has emerged as a promising non-invasive approach to Autism Spectrum Disorder (ASD) screening, with systematic differences in attentional allocation and revisi…
GRACE: LLM-Grounded Semantic Metric Spaces for Scalable Mixed-Data Clustering
Zihua Yang, Zhencheng Xie, Junyang Chen +4
Clustering mixed tabular data requires a unified metric space to bridge the inherent heterogeneity between continuous numerical measurements and discrete categorical symbols. Tradi…
Bridge the Gaps: Heterogeneous Attributed Graph Clustering via Quaternion Representation Learning
Xinxi Chen, Junyang Chen, Yiqun Zhang +2
Attributed graph clustering partitions nodes by jointly exploiting node attributes and graph topology. It remains challenging due to attribute heterogeneity and representation degr…
Fine-Tuning Impairs the Balancedness of Foundation Models in Long-tailed Personalized Federated Learning
Shihao Hou, Chikai Shang, Zhiheng Yang +5
Personalized federated learning (PFL) with foundation models has emerged as a promising paradigm enabling clients to adapt to heterogeneous data distributions. However, real-world…
SECOS: Semantic Capture for Rigorous Classification in Open-World Semi-Supervised Learning
Hezhao Liu, Jiacheng Yang, Junlong Gao +4
In open-world semi-supervised learning (OWSSL), a model learns from labeled data and unlabeled data containing both known and novel classes. In practical OWSSL applications, models…
Learning from Imperfect Text Guidance: Robust Long-Tail Visual Recognition with High-Noise Label
Mengke Li, Haiquan Ling, Yiqun Zhang +2
Real-world data often exhibit long-tailed distributions with numerous noisy labels, substantially degrading the performance of deep models. While prior research has made progress i…
Learning Unified Distance Metric for Heterogeneous Attribute Data Clustering
Yiqun Zhang, Mingjie Zhao, Yizhou Chen +2
Datasets composed of numerical and categorical attributes (also called mixed data hereinafter) are common in real clustering tasks. Differing from numerical attributes that indicat…
Learning Order Forest for Qualitative-Attribute Data Clustering
Mingjie Zhao, Sen Feng, Yiqun Zhang +3
Clustering is a fundamental approach to understanding data patterns, wherein the intuitive Euclidean distance space is commonly adopted. However, this is not the case for implicit…
Detecting Autism Spectrum Disorder with Deep Eye Movement Features
Zhanpei Huang, Taochen chen, Fangqing Gu +1
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and behavioral patterns. Eye movement data offers a non-invasive d…
Break the Tie: Learning Cluster-Customized Category Relationships for Categorical Data Clustering
Mingjie Zhao, Zhanpei Huang, Yang Lu +4
Categorical attributes with qualitative values are ubiquitous in cluster analysis of real datasets. Unlike the Euclidean distance of numerical attributes, the categorical attribute…
Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations
Camilo Tamayo-Rousseau, Yunjia Zhao, Yiqun Zhang +1
Self-attention mechanisms are foundational to Transformer architectures, supporting their impressive success in a wide range of tasks. While there are many self-attention variants,…
FATE: A Prompt-Tuning-Based Semi-Supervised Learning Framework for Extremely Limited Labeled Data
Hezhao Liu, Yang Lu, Mengke Li +4
Semi-supervised learning (SSL) has achieved significant progress by leveraging both labeled data and unlabeled data. Existing SSL methods overlook a common real-world scenario when…
Classifying Long-tailed and Label-noise Data via Disentangling and Unlearning
Chen Shu, Mengke Li, Yiqun Zhang +4
In real-world datasets, the challenges of long-tailed distributions and noisy labels often coexist, posing obstacles to the model training and performance. Existing studies on long…
PRO-VPT: Distribution-Adaptive Visual Prompt Tuning via Prompt Relocation
Chikai Shang, Mengke Li, Yiqun Zhang +5
Visual prompt tuning (VPT), i.e., fine-tuning some lightweight prompt tokens, provides an efficient and effective approach for adapting pre-trained models to various downstream tas…
Asynchronous Federated Clustering with Unknown Number of Clusters
Yunfan Zhang, Yiqun Zhang, Yang Lu +3
Federated Clustering (FC) is crucial to mining knowledge from unlabeled non-Independent Identically Distributed (non-IID) data provided by multiple clients while preserving their p…
HyReaL: Clustering Attributed Graph via Hyper-Complex Space Representation Learning
Junyang Chen, Yang Lu, Mengke Li +3
Clustering complex data in the form of attributed graphs has attracted increasing attention, where powerful graph representation is a critical prerequisite. However, the well-known…
Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition
Mengke Li, Ye Liu, Yang Lu +3
Long-tail learning has garnered widespread attention and achieved significant progress in recent times. However, even with pre-trained prior knowledge, models still exhibit weaker…
Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning
Fengling Lv, Xinyi Shang, Yang Zhou +3
Personalized Federated Learning (PFL) aims to acquire customized models for each client without disclosing raw data by leveraging the collective knowledge of distributed clients. H…