48 citations · 69 across the 13 of their papers we have counts for
17 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…
AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE
Tao Xie, Zexi Tan, Haoyi Xiao +5
Multivariate time series classification (MTSC) is pivotal in high-stakes domains, such as clinical diagnosis and industrial fault detection, where safe deployment necessitates tran…
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…
Stitch the Fragments: One-Shot Hierarchical Federated Clustering
Shenghong Cai, Zihua Yang, Yang Lu +4
Federated Clustering (FC) faces a critical bottleneck in real-world scenarios, i.e., global clusters are rarely intact, often fragmenting into incomplete, multi-granular unlabeled…
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…
Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis
Mengke Li, Lihao Chen, Peng Zhang +2
Parameter-efficient fine-tuning strategies for foundation models in 1D textual and 2D visual analysis have demonstrated remarkable efficacy. However, due to the scarcity of point c…
PI-H2T: Enhancing Long-Tailed Visual Recognition with Permutation-Invariant and Head-to-Tail Feature Fusion
Mengke Li, Zhikai Hu, Yang Lu +3
The imbalanced distribution of long-tailed data presents a significant challenge for deep learning models, causing them to prioritize head classes while neglecting tail classes. Tw…
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…
Fuse4Seg: Image Fusion for Multi-Modal Medical Segmentation via Bi-level Optimization
Yuchen Guo, Junli Gong, Hongmin Cai +2
Multi-modal medical image fusion is traditionally optimized for human visual perception, aiming to maximize generic contrast and structural fidelity. However, when these visually p…
Adapt PointFormer: 3D Point Cloud Analysis via Adapting 2D Visual Transformers
Mengke Li, Da Li, Guoqing Yang +2
Pre-trained large-scale models have exhibited remarkable efficacy in computer vision, particularly for 2D image analysis. However, when it comes to 3D point clouds, the constrained…
Improve Knowledge Distillation via Label Revision and Data Selection
Weichao Lan, Yiu-ming Cheung, Qing Xu +4
Knowledge distillation (KD) has become a widely used technique in the field of model compression, which aims to transfer knowledge from a large teacher model to a lightweight stude…
Federated Learning with Extremely Noisy Clients via Negative Distillation
Yang Lu, Lin Chen, Yonggang Zhang +4
Federated learning (FL) has shown remarkable success in cooperatively training deep models, while typically struggling with noisy labels. Advanced works propose to tackle label noi…
Feature Fusion from Head to Tail for Long-Tailed Visual Recognition
Mengke Li, Zhikai Hu, Yang Lu +3
The imbalanced distribution of long-tailed data presents a considerable challenge for deep learning models, as it causes them to prioritize the accurate classification of head clas…