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We present a framework called Acquired Deep Impressions (ADI) whichcontinuously learns knowledge of objects as "impressions" for compositionalscene understanding. In this framework, the model first acquires knowledge fromscene images containing a single object in a supervised manner, and thencontinues to learn from novel multi-object scene images which may containobjects that have not been seen before without any further supervision, underthe guidance of the learned knowledge as humans do. By memorizing impressionsof objects into parameters of neural networks and applying the generativereplay strategy, the learned knowledge can be reused to generate images withpseudo-annotations and in turn assist the learning of novel scenes. Theproposed ADI framework focuses on the acquisition and utilization of knowledge,and is complementary to existing deep generative models proposed forcompositional scene representation. We adapt a base model to make it fallwithin the ADI framework and conduct experiments on two types of datasets.Empirical results suggest that the proposed framework is able to effectivelyutilize the acquired impressions and improve the scene decompositionperformance.

Knowledge-Guided Object Discovery with Acquired Deep Impressions