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20242026
most citedLearning Unified Distance Metric for Heterogeneous Attribute Data Clustering

26 citations · 44 across the 11 of their papers we have counts for

collaborators

17 papers

cs.CV2026

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…

cs.AI2026

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…

cs.LG2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…