Speech-based diaries from mobile phones can capture paralinguistic patterns that help
detect mental illness symptoms such as suicidal ideation. However, previous studies have …

Deep learning has offered advanced analytical capabilities to enhance the accuracy and
efficiency of detecting COVID-19 through complex pattern recognition in medical imaging …

Cross-scene classification focuses on setting up an effective domain adaptation (DA) way to
transfer the learnable knowledge from source to target domain, which can be reasonably …

Most domain adaptation (DA) methods are based on either a convolutional neural networks
(CNNs) or a vision transformers (ViTs). They align the distribution differences between …

The Vision Transformer (ViT) model serves as a powerful model to capture and comprehend
global information, particularly when trained on extensive datasets. Conversely, the …

Event-based semantic segmentation has gained popularity due to its capability to deal with
scenarios under high-speed motion and extreme lighting conditions which cannot be …

Roadside perception can greatly increase the safety of autonomous vehicles by extending
their perception ability beyond the visual range and addressing blind spots. However …

Abstract Semi-Supervised Domain Adaptation (SSDA) leverages knowledge from a fully
labeled source domain to classify data in a partially labeled target domain. Due to the limited …

Existing semi-supervised domain adaptation (SSDA) models have exhibited impressive
performance on the target domain by effectively utilizing few labeled target samples per …

Egocentric activity recognition is a prominent computer vision task that is based on the use
of wearable cameras. Since egocentric videos are captured through the perspective of the …