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Context reasoning is critical in a wide variety of applications where currentinputs need to be interpreted in the light of previous experience andknowledge. Both spatial and temporal contextual information play a criticalrole in the domain of visual recognition. Here we investigate spatialconstraints (what image features provide contextual information and where theyare located), and temporal constraints (when different contextual cues matter)for visual recognition. The task is to reason about the scene context and inferwhat a target object hidden behind a flap is in a natural image. To tackle thisproblem, we first describe an online human psychophysics experiment recordingactive sampling via mouse clicks in lift-the-flap games and identify clickingpatterns and features which are diagnostic for high contextual reasoningaccuracy. As a proof of the usefulness of these clicking patterns and visualfeatures, we extend a state-of-the-art recurrent model capable of attending tosalient context regions, dynamically integrating useful information, makinginferences, and predicting class label for the target object over multipleclicks. The proposed model achieves human-level contextual reasoning accuracy,shares human-like sampling behavior and learns interpretable features forcontextual reasoning.

Lift-the-flap: what, where and when for context reasoning