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Transfer learning has become the de facto standard in computer vision andnatural language processing, especially where labeled data is scarce. Accuracycan be significantly improved by using pre-trained models and subsequentfine-tuning. In visual reasoning tasks, such as image question answering,transfer learning is more complex. In addition to transferring the capabilityto recognize visual features, we also expect to transfer the system's abilityto reason. Moreover, for video data, temporal reasoning adds another dimension.In this work, we formalize these unique aspects of transfer learning andpropose a theoretical framework for visual reasoning, exemplified by thewell-established CLEVR and COG datasets. Furthermore, we introduce a new,end-to-end differentiable recurrent model (SAMNet), which showsstate-of-the-art accuracy and better performance in transfer learning on bothdatasets. The improved performance of SAMNet stems from its capability todecouple the abstract multi-step reasoning from the length of the sequence andits selective attention enabling to store only the question-relevant objects inthe external memory.

Transfer Learning in Visual and Relational Reasoning