research
∙
09/18/2023
Empirical Study of Mix-based Data Augmentation Methods in Physiological Time Series Data
Data augmentation is a common practice to help generalization in the pro...
share
research
∙
03/31/2023
Weakly-Supervised Text-driven Contrastive Learning for Facial Behavior Understanding
Contrastive learning has shown promising potential for learning robust r...
research
∙
11/21/2022
PiRL: Participant-Invariant Representation Learning for Healthcare
Due to individual heterogeneity, performance gaps are observed between g...
research
∙
10/13/2022
LEAVES: Learning Views for Time-Series Data in Contrastive Learning
Contrastive learning, a self-supervised learning method that can learn r...
research
∙
10/13/2022
Empirical Evaluation of Data Augmentations for Biobehavioral Time Series Data with Deep Learning
Deep learning has performed remarkably well on many tasks recently. Howe...
research
∙
09/25/2022
Multimodal Learning with Channel-Mixing and Masked Autoencoder on Facial Action Unit Detection
Recent studies utilizing multi-modal data aimed at building a robust mod...
research
∙
03/29/2022
An EEG-Based Multi-Modal Emotion Database with Both Posed and Authentic Facial Actions for Emotion Analysis
Emotion is an experience associated with a particular pattern of physiol...
research
∙
03/23/2022
Your "Attention" Deserves Attention: A Self-Diversified Multi-Channel Attention for Facial Action Analysis
Visual attention has been extensively studied for learning fine-grained ...
research
∙
02/16/2022
More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors
Accurately recognizing health-related conditions from wearable data is c...
research
∙
12/27/2021
Over-the-Air Multi-Task Federated Learning Over MIMO Interference Channel
With the explosive growth of data and wireless devices, federated learni...
research
∙
06/02/2020
