4 accepted papers
The continuous emergence of new objects in the visual world poses a serious challenge to deep object recognition methods, which sparks the increasing study on continual or incremental learning. However, learning new tasks faces the tough catastrophic forgetting problem, i.e., dramatic performance de…
Motor Imagery (MI) is essential in Brain-Computer Interfaces (BCIs), highlighting the central role of electroencephalography (EEG) in this technology. However, the amount of raw EEG data is often limited. Raw EEG data contains significant noise caused by individual and task-specific differences. The…
A brain-computer interface (BCI) offers an innovative solution for facilitating communication and control in individuals with paralysis. BCI reflects brain activity by decoding electroencephalogram (EEG) signals. Despite numerous techniques for classifying motor imagery (MI) EEG signals, challenges…
Learning with few labeled data is a key challenge for visual recognition, as deep neural networks tend to overfit using a few samples only. One of the Few-shot learning methods called metric learning addresses this challenge by first learning a deep distance metric to determine whether a pair of ima…