3 accepted papers
In supervised learning, traditional image masking faces two key issues: (i) discarded pixels are underutilized, leading to a loss of valuable contextual information; (ii) masking may remove small or critical features, especially in fine-grained tasks. In contrast, masked image modeling (MIM) has dem
Electroencephalography (EEG) is vital for brain-computer interfaces (BCIs) due to its non-invasive approach and high temporal resolution data capabilities, amid challenges such as data scarcity and the need for extensive labeling. Significant inter-individual variability in EEG signals further limit…
For the few-shot segmentation (FSS) task, existing methods attempt to capture the diversity of new classes by fully utilizing the limited support images, such as cross-attention and prototype matching. However, they often overlook the fact that there is variability in different regions of the same o…