177 citations · 631 across the 42 of their papers we have counts for
42 papers
Auto-Augmentation Contrastive Learning for Wearable-based Human Activity Recognition
Qingyu Wu, Jianfei Shen, Feiyi Fan +3
For low-semantic sensor signals from human activity recognition (HAR), contrastive learning (CL) is essential to implement novel applications or generic models without manual annot…
Think Before You Move: Latent Motion Reasoning for Text-to-Motion Generation
Yijie Qian, Juncheng Wang, Yuxiang Feng +7
Current state-of-the-art paradigms predominantly treat Text-to-Motion (T2M) generation as a direct translation problem, mapping symbolic language directly to continuous poses. Whil…
Learning Critically: Selective Self Distillation in Federated Learning on Non-IID Data
Yuting He, Yiqiang Chen, XiaoDong Yang +3
Federated learning (FL) enables multiple clients to collaboratively train a global model while keeping local data decentralized. Data heterogeneity (non-IID) across clients has imp…
FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcare
Penghao Wang, Qian Chen, Teng Zhang +3
Federated Learning (FL) has emerged as an effective solution for multi-institutional collaborations without sharing patient data, offering a range of methods tailored for diverse a…
A Survey on Unlearnable Data
Jiahao Li, Yiqiang Chen, Yunbing Xing +2
Unlearnable data (ULD) has emerged as an innovative defense technique to prevent machine learning models from learning meaningful patterns from specific data, thus protecting data…
Ten Challenging Problems in Federated Foundation Models
Tao Fan, Hanlin Gu, Xuemei Cao +30
Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of fed…
Ultra-High Resolution Segmentation via Boundary-Enhanced Patch-Merging Transformer
Haopeng Sun, Yingwei Zhang, Lumin Xu +2
Segmentation of ultra-high resolution (UHR) images is a critical task with numerous applications, yet it poses significant challenges due to high spatial resolution and rich fine d…
Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application
Chuanpeng Yang, Wang Lu, Yao Zhu +5
Large Language Models (LLMs) have showcased exceptional capabilities in various domains, attracting significant interest from both academia and industry. Despite their impressive p…
AUGlasses: Continuous Action Unit based Facial Reconstruction with Low-power IMUs on Smart Glasses
Yanrong Li, Tengxiang Zhang, Xin Zeng +3
Recent advancements in augmented reality (AR) have enabled the use of various sensors on smart glasses for applications like facial reconstruction, which is vital to improve AR exp…
Generative AI for Synthetic Data Generation: Methods, Challenges and the Future
Xu Guo, Yiqiang Chen
The recent surge in research focused on generating synthetic data from large language models (LLMs), especially for scenarios with limited data availability, marks a notable shift…
Self-supervised Learning for Electroencephalogram: A Systematic Survey
Weining Weng, Yang Gu, Shuai Guo +4
Electroencephalogram (EEG) is a non-invasive technique to record bioelectrical signals. Integrating supervised deep learning techniques with EEG signals has recently facilitated au…
Differentially Private Pre-Trained Model Fusion using Decentralized Federated Graph Matching
Qian Chen, Yiqiang Chen, Xinlong Jiang +5
Model fusion is becoming a crucial component in the context of model-as-a-service scenarios, enabling the delivery of high-quality model services to local users. However, this appr…
ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning
Wang Lu, Hao Yu, Jindong Wang +6
When personalized federated learning (FL) meets large foundation models, new challenges arise from various limitations in resources. In addition to typical limitations such as data…
GestureGPT: Toward Zero-Shot Free-Form Hand Gesture Understanding with Large Language Model Agents
Xin Zeng, Xiaoyu Wang, Tengxiang Zhang +3
Existing gesture interfaces only work with a fixed set of gestures defined either by interface designers or by users themselves, which introduces learning or demonstration efforts…
A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation
Weining Weng, Yang Gu, Qihui Zhang +3
Due to the abundant neurophysiological information in the electroencephalogram (EEG) signal, EEG signals integrated with deep learning methods have gained substantial traction acro…
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization
Wang Lu, Jindong Wang, Xinwei Sun +4
Time series remains one of the most challenging modalities in machine learning research. The out-of-distribution (OOD) detection and generalization on time series tend to suffer du…
FedBone: Towards Large-Scale Federated Multi-Task Learning
Yiqiang Chen, Teng Zhang, Xinlong Jiang +3
Heterogeneous federated multi-task learning (HFMTL) is a federated learning technique that combines heterogeneous tasks of different clients to achieve more accurate, comprehensive…
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning
Xin Qin, Jindong Wang, Shuo Ma +4
Human activity recognition (HAR) is a time series classification task that focuses on identifying the motion patterns from human sensor readings. Adequate data is essential but a m…
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…
Out-of-Distribution Representation Learning for Time Series Classification
Wang Lu, Jindong Wang, Xinwei Sun +2
Time series classification is an important problem in real world. Due to its non-stationary property that the distribution changes over time, it remains challenging to build models…
Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution
Wang Lu, Jindong Wang, Yidong Wang +1
The distribution shifts between training and test data typically undermine the performance of models. In recent years, lots of work pays attention to domain generalization (DG) whe…
Domain Generalization for Activity Recognition via Adaptive Feature Fusion
Xin Qin, Jindong Wang, Yiqiang Chen +2
Human activity recognition requires the efforts to build a generalizable model using the training datasets with the hope to achieve good performance in test datasets. However, in r…
Domain-invariant Feature Exploration for Domain Generalization
Wang Lu, Jindong Wang, Haoliang Li +2
Deep learning has achieved great success in the past few years. However, the performance of deep learning is likely to impede in face of non-IID situations. Domain generalization (…
Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition
Wang Lu, Jindong Wang, Yiqiang Chen +3
It is expensive and time-consuming to collect sufficient labeled data to build human activity recognition (HAR) models. Training on existing data often makes the model biased towar…