Multi-modal Active Learning From Human Data: A Deep Reinforcement Learning Approach
Multi-modal Active Learning From Human Data: A Deep Reinforcement Learning Approach
Human behavior expression and experience are inherently multimodal, and characterized by vast individual and contextual heterogeneity. To achieve meaningful human-computer and human-robot interactions, multi-modal models of the user's states (e.g., engagement) are therefore needed. Most of the existing works that try to build classifiers for the user's states assume that …