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This paper aims at task-oriented action prediction, i.e., predicting asequence of actions towards accomplishing a specific task under a certainscene, which is a new problem in computer vision research. The main challengeslie in how to model task-specific knowledge and integrate it in the learningprocedure. In this work, we propose to train a recurrent long-short term memory(LSTM) network for handling this problem, i.e., taking a scene image (includingpre-located objects) and the specified task as input and recurrently predictingaction sequences. However, training such a network usually requires largeamounts of annotated samples for covering the semantic space (e.g., diverseaction decomposition and ordering). To alleviate this issue, we introduce atemporal And-Or graph (AOG) for task description, which hierarchicallyrepresents a task into atomic actions. With this AOG representation, we canproduce many valid samples (i.e., action sequences according with common sense)by training another auxiliary LSTM network with a small set of annotatedsamples. And these generated samples (i.e., task-oriented action sequences)effectively facilitate training the model for task-oriented action prediction.In the experiments, we create a new dataset containing diverse daily tasks andextensively evaluate the effectiveness of our approach.

Knowledge-Guided Recurrent Neural Network Learning for Task-Oriented Action Prediction