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The visual system processes a scene using a sequence of selective glimpses,each driven by spatial and object-based attention. These glimpses reflect whatis relevant to the ongoing task and are selected through recurrent processingand recognition of the objects in the scene. In contrast, most models treatattention selection and recognition as separate stages in a feedforwardprocess. Here we show that using capsule networks to create an object-centrichidden representation in an encoder-decoder model with iterative glimpseattention yields effective integration of attention and recognition. Weevaluate our model on three multi-object recognition tasks; highly overlappingdigits, digits among distracting clutter and house numbers, and show that itlearns to effectively move its glimpse window, recognize and reconstruct theobjects, all with only the classification as supervision. Our work takes a steptoward a general architecture for how to integrate recurrent object-centricrepresentation into the planning of attentional glimpses.

Recurrent Attention Models with Object-centric Capsule Representation for Multi-object Recognition