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Sparse Prototype Network for Explainable Pedestrian Behavior Prediction
Yan Feng, Alexander Carballo, Kazuya Takeda
Predicting pedestrian behavior is challenging yet crucial for applications such as autonomous driving and smart city. Recent deep learning models have achieved remarkable performan…
R-Cut: Enhancing Explainability in Vision Transformers with Relationship Weighted Out and Cut
Yingjie Niu, Ming Ding, Maoning Ge +3
Transformer-based models have gained popularity in the field of natural language processing (NLP) and are extensively utilized in computer vision tasks and multi-modal models such…
Learning a Model for Inferring a Spatial Road Lane Network Graph using Self-Supervision
Robin Karlsson, David Robert Wong, Simon Thompson +1
Interconnected road lanes are a central concept for navigating urban roads. Currently, most autonomous vehicles rely on preconstructed lane maps as designing an algorithmic model i…