  Convolutional neural networks have been shown to develop internal
representations, which correspond closely to semantically meaningful objects
and parts, although trained solely on class labels. Class Activation Mapping
(CAM) is a recent method that makes it possible to easily highlight the image
regions contributing to a network's classification decision. We build upon
these two developments to enable a network to re-examine informative image
regions, which we term introspection. We propose a weakly-supervised iterative
scheme, which shifts its center of attention to increasingly discriminative
regions as it progresses, by alternating stages of classification and
introspection. We evaluate our method and show its effectiveness over a range
of several datasets, where we obtain competitive or state-of-the-art results:
on Stanford-40 Actions, we set a new state-of the art of 81.74%. On
FGVC-Aircraft and the Stanford Dogs dataset, we show consistent improvements
over baselines, some of which include significantly more supervision.
