The goal of this research is to combine vision with aspects of language and social cognition to obtain complex knowledge about the environment. To obtain a full understanding of visual scenes, computational models should be able to extract from the scene any meaningful information that a human observer can extract about actions, agents, goals, scenes and object configurations, social interactions, and more. We refer to this as the ‘Turing test for vision,’ i.e., the ability of a machine to use vision to answer a large and flexible set of queries about objects and agents in an image in a human-like manner. Queries might be about objects, their parts, and spatial relations between objects, actions, goals, and interactions. Understanding queries and formulating answers requires interactions between vision and natural language. Interpreting goals and interactions requires connections between vision and social cognition. Answering queries also requires task-dependent processing, i.e., different visual processes to achieve different goals.
We are constructing detectors which can determine gaze direction in 3D and to apply these detectors to model human-human and human-object interactions. We aim to predict the intentions and goals of the agents by utilizing their direction of gaze, head pose, body pose, and the spatial relations between agents and objects.
