Search Results for author:
Found 9 papers, 3 papers with code
Self-supervised Learning for Electroencephalogram: A Systematic Survey
2) We provide a comprehensive review of SSL for EEG analysis, including taxonomy, methodology, and technique details of the existing EEG-based SSL frameworks, and discuss the difference between these methods.
A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation
Due to the abundant neurophysiological information in the electroencephalogram (EEG) signal, EEG signals integrated with deep learning methods have gained substantial traction across numerous real-world tasks.
Unsupervised Deep Cross-Language Entity Alignment
We outperformed the state-of-the-art method in unsupervised and semi-supervised categories.
An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos
Emotion recognition in user-generated videos plays an important role in human-centered computing.
Ranked #4 on
Video Emotion Recognition
on Ekman6
Multi-source Distilling Domain Adaptation
Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA).
Domain Adaptation
Multi-Source Unsupervised Domain Adaptation
Multi-source Domain Adaptation for Semantic Segmentation
In this paper, we propose to investigate multi-source domain adaptation for semantic segmentation.
Ranked #2 on
Domain Adaptation
on GTA5+Synscapes to Cityscapes
A Mobile Cloud Collaboration Fall Detection System Based on Ensemble Learning
Falls are one of the important causes of accidental or unintentional injury death worldwide.
WiFi based trajectory alignment, calibration and easy site survey using smart phones and foot-mounted IMUs
Foot-mounted inertial positioning (FMIP) can face problems of inertial drifts and unknown initial states in real applications, which renders the estimated trajectories inaccurate and not obtained in a well defined coordinate system for matching trajectories of different users.
Joint Positioning and Radio Map Generation Based on Stochastic Variational Bayesian Inference for FWIPS
This SVBI based position and RSS estimation has three properties: i) being able to predict the distribution of the estimated position and RSS, ii) treating each observation of RSS at each RP as an example to learn for FbP and RM generation instead of using the whole RM as an example, and iii) requiring only one time training of the SVBI model for both localization and RSS estimation.
