Visual Curiosity: Learning to Ask Questions to Learn Visual Recognition
; Proceedings of The 2nd Conference on Robot Learning, PMLR 87:63-80, 2018.
In an open-world setting, it is inevitable that an intelligent agent (e.g., a robot) will encounter visual objects, attributes or relationships it does not recognize. In this work, we develop an agent empowered with visual curiosity, i.e. the ability to ask questions to an Oracle (e.g., human) about the contents in images (e.g., ‘What is the object on the left side of the red cube?’) and build visual recognition model based on the answers received (e.g., ‘Cylinder’). In order to do this, the agent must (1) understand what it recognizes and what it does not, (2) formulate a valid, unambiguous and informative ‘language’ query (a question) to ask the Oracle, (3) derive the parameters of visual classifiers from the Oracle response and (4) leverage the updated visual classifiers to ask more clarified questions. Specifically, we propose a novel framework and formulate the learning of visual curiosity as a reinforcement learning problem. In this framework, all components of our agent – visual recognition module (to see), question generation policy (to ask), answer digestion module (to understand) and graph memory module (to memorize) – are learned entirely end-to-end to maximize the reward derived from the scene graph obtained by the agent as a consequence of the dialog with the Oracle. Importantly, the question generation policy is disentangled from the visual recognition system and specifics of the ‘environment’ (scenes). Consequently, we demonstrate a sort of ‘double’ generalization – our question generation policy generalizes to new environments and a new pair of eyes, i.e., new visual system. Specifically, an agent trained on one set of environments (scenes) and with one particular visual recognition system is able to ask intelligent questions about new scenes when paired with a new visual recognition system. Trained on a synthetic dataset, our results show that our agent learns new visual concepts significantly faster than several heuristic baselines – even when tested on synthetic environments with novel objects, as well as in a realistic environment.