5 accepted papers
As the pretraining-finetuning paradigm becomes dominant in modern AI, the security of model supply chains faces new risks from backdoor attacks. Existing work primarily studies backdoors injected during pretraining and treats subsequent finetuning with clean data as a defense, while recent finetunin
Alzheimer’s disease (AD) alters brain electrophysiology and disrupts multichannel EEG dynamics, making accurate and clinically useful EEG-based diagnosis increasingly important for screening and disease monitoring. However, many existing approaches rely on black-box classifiers and do not explicitly…
Few-shot EEG domain adaptation faces severe data heterogeneity and optimization instability. While prevalent "symmetric alignment" methods typically seek a compromised shared subspace, they often falter when domain discrepancies are vast, leading to mutual interference and negative transfer. To over…
To detect prohibited items in challenging categories, human inspectors typically rely on images from two distinct views (vertical and side). Can AI detect prohibited items from dual-view X-ray images in the same way humans do? Existing X-ray datasets often suffer from limitations, such as single-vie…
Emotion recognition based on multimodal physiological signals is attracting more and more attention. However, how to deal with the consistency and heterogeneity of multimodal physiological signals, as well as individual differences across subjects, pose two significant challenges. In this paper, we…