7 accepted papers
Translating brain activity into meaningful visual content has long been recognized as a fundamental challenge in neuroscience and brain-computer interface research. Recent advances in EEG-based neural decoding have shown promise, yet two critical limitations remain in this area: poor generalization…
Auditory attention detection (AAD) from electroencephalography (EEG) signals has garnered significant interest for its potential in brain-computer interfaces and hearing aids. Nevertheless, accurate decoding remains challenging due to the high-dimensional, non-stationary, and inherently noisy charac…
In monocular depth estimation, effective acquisition of global and local information is the key to improving accuracy. We introduce a novel dual branch network called Swin Vision Transformer Net (SVTNet), where the Swin Transformer and Vision Transformer are combined to learn features with global an…
In prior studies, domain adversarial neural networks (DANNs) are used to align image-level features regardless of foreground and background. However, the conventional discriminator in DANNs may leads the feature extractor to disregard cross-domain features rather than aligning them. This phenomenon…
The accurate segmentation of the right and left ventricles with limited labeled data is a challenging task in echocardiographic data analysis. To fully leverage the easily accessible unlabeled data, we propose a label-decoupling teacher-student framework (LDTSF) based on semi-supervised learning. Sp…
An automatic vision-based sewer inspection plays a key role of sewage system in a modern city. Recent advances focus on utilizing deep learning model to realize the sewer inspection system, benefiting from the capability of data-driven feature representation. However, the inherent uncertainty of sew…
Recently, local-editing-based transformations are introduced in anomaly detection for defect visual inspection, which construct a pretext task with the paradigm of self-supervised learning. However, supervised information of local-editing-based transformation may be incorrect when invalid trans-form…