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The process of identifying changes or transformations in a scene along withthe ability of reasoning about their causes and effects, is a key aspect ofintelligence. In this work we go beyond recent advances in computationalperception, and introduce a more challenging task, Image-based Event-Sequencing(IES). In IES, the task is to predict a sequence of actions required torearrange objects from the configuration in an input source image to the one inthe target image. IES also requires systems to possess inductivegeneralizability. Motivated from evidence in cognitive development, we compilethe first IES dataset, the Blocksworld Image Reasoning Dataset (BIRD) whichcontains images of wooden blocks in different configurations, and the sequenceof moves to rearrange one configuration to the other. We first explore the useof existing deep learning architectures and show that these end-to-end methodsunder-perform in inferring temporal event-sequences and fail at inductivegeneralization. We then propose a modular two-step approach: Visual Perceptionfollowed by Event-Sequencing, and demonstrate improved performance by combininglearning and reasoning. Finally, by showing an extension of our approach onnatural images, we seek to pave the way for future research on event sequencingfor real world scenes.

Blocksworld Revisited: Learning and Reasoning to Generate Event-Sequences from Image Pairs