In this paper, we propose a novel approach that learns to sequentially attend to differentConvolutional Neural Networks (CNN) layers (i.e., “what” feature abstraction to attend to) and different spatial locations of the selected feature map (i.e., “where”) to performthe task at hand. Specifically, at each Recurrent Neural Network (RNN) step, both a CNN layer and localized spatial region within it are selected for further processing. We demonstrate the effectiveness of this approach on two computer vision tasks: (i) image-basedsix degree of freedom camera pose regression and (ii) indoor scene classification. Em-pirically, we show that combining the “what” and “where” aspects of attention improvesnetwork performance on both tasks. We evaluate our method on standard benchmarks forcamera localization (Cambridge, 7-Scenes, and TUM-LSI) and for scene classification(MIT-67 Indoor Scenes). For camera localization our approach reduces the median errorby 18.8% for position and 8.2% for orientation (averaged over all scenes), and for sceneclassification it improves the mean accuracy by 3.4% over previous methods.
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