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Exploring goal taxonomies using the goal-based outcome tool in children and young people's mental health settings
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Self-supervised extracted contrast network for facial expression recognition
Get PDFSelf-supervised Contrastive learning has recently demonstrated significant performance in Facial Expression Recognition (FER). However, existing methods fail to address inherent challenges such as similar and blurred facial expressions, and low-quality semantic pairs from image cropping. To tackle these challenges, this paper proposes a Lightweight Self-supervised Distilled Contrastive network for FER (LSDC-FER), which consists of a center suppressed cropping module and a multi-scale feature joint distillation module. The former improves the quality of face expression semantic pairs by face image localization and center suppressed cropping. The latter learns fine-grained features using knowledge distillation and explores multi-scale contextual information to improve feature learning. Experimental results show that LSDC-FER achieves 75.65% and 61.16% recognition accuracy on RAF-DB and FER-2013 public facial expression datasets, respectively
Collaborative Learning for Incremental Classification of EMG Signals
Get PDFIn this work, we address the problem of online learning for electromyogram (EMG) classification. The main challenge of EMG is its streaming nature, which implies that not all data are available at once. As such, offline and batch methods such as deep learning are generally not appropriate for such small data. To address this problem, we propose an incremental learning technique whereby the algorithm enhances its knowledge about the data progressively, as it receives new data. Moreover, we propose to employ multiple learners (instead of a single learner); each learning and focusing on a specific class. This federated learning strategy in incremental learning shows superior performance over both existing incremental learning and deep learning methods in our simulation studies on small data
Invisible, Unreadable, and Inaudible Cookie Notices:An Evaluation of Cookie Notices for Users with Visual Impairments
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