WeDea A New EEG-based Framework for Emotion Recognition

Abstract: Model parameters store pretrained deep models’ knowledge. These parameters give trained models “memory” to generalize well on unseen data. In the absence of training data, a trained model is only useful for inference or…

Abstract: Due to its many real-world applications—evacuation situation analysis, intelligent transport system deployment, traffic operations—human trajectory forecasting has been a field of active research for decades. We model human trajectory forecasting as learning a representation…

Abstract: Deep learning (DL) requires massive data collection. DL is rarely used in applications without a large dataset. Getting enough data takes time, money, and effort. The scientific community uses synthetic images and real data…

Abstract: ReID is difficult due to person images’ large pose variations and misalignment errors. Existing works use pose estimation, part segmentation, etc. to improve pedestrian representations. Boosting ReID accuracy requires computational overheads and makes deep…

Abstract: This article examines neuroscience, network science, and dynamic systems research on brain-inspired intelligence. Instead of using artificial computing networks, reconstructing cortical networks with dynamic activities that nourish brain functions can replicate brain intelligence. The…

Abstract: Pattern recognition, data mining, and machine learning all face clustering issues. Traditional shallow-structured clustering algorithms cannot uncover the interdependence of complex data features in latent space. Since they can learn promising latent representations from…