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End-to-end trained Recurrent Neural Networks (RNNs) have been successfullyapplied to numerous problems that require processing sequences, such as imagecaptioning, machine translation, and text recognition. However, RNNs oftenstruggle to generalise to sequences longer than the ones encountered duringtraining. In this work, we propose to optimise neural networks explicitly forinduction. The idea is to first decompose the problem in a sequence ofinductive steps and then to explicitly train the RNN to reproduce such steps.Generalisation is achieved as the RNN is not allowed to learn an arbitraryinternal state; instead, it is tasked with mimicking the evolution of a validstate. In particular, the state is restricted to a spatial memory map thattracks parts of the input image which have been accounted for in previoussteps. The RNN is trained for single inductive steps, where it produces updatesto the memory in addition to the desired output. We evaluate our method on twodifferent visual recognition problems involving visual sequences: (1) textspotting, i.e. joint localisation and reading of text in images containingmultiple lines (or a block) of text, and (2) sequential counting of objects inaerial images. We show that inductive training of recurrent models enhancestheir generalisation ability on challenging image datasets.

Inductive Visual Localisation: Factorised Training for Superior Generalisation