Abductive Action Inference
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Abductive reasoning aims to make the most likely inference for a given set of incomplete observations. In this work, given a situation or a scenario, we aim to answer the question 'what is the set of actions that were executed by the human in order to come to this current state?', which we coin as abductive action inference. We provide a solution based on the human-object relations and their states in the given scene. Specifically, we first detect objects and humans in the scene, and then generate representations for each human-centric relation. Using these human-centric relations, we derive the most likely set of actions the human may have executed to arrive in this state. To generate human-centric relational representations, we investigate several models such as Transformers, a novel graph neural network-based encoder-decoder, and a new relational bilinear pooling method. We obtain promising results using these new models on this challenging task on the Action Genome dataset.
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