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arXiv · 1812.02626

Guided Zoom: Questioning Network Evidence for Fine-grained Classification

Sarah Adel Bargal·Andrea Zunino·Vitali Petsiuk·Jianming Zhang·Kate Saenko·Vittorio Murino·Stan Sclaroff
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Abstract

We propose Guided Zoom, an approach that utilizes spatial grounding of a model's decision to make more informed predictions. It does so by making sure the model has "the right reasons" for a prediction, defined as reasons that are coherent with those used to make similar correct decisions at training time. The reason/evidence upon which a deep convolutional neural network makes a prediction is defined to be the spatial grounding, in the pixel space, for a specific class conditional probability in the model output. Guided Zoom examines how reasonable such evidence is for each of the top-k predicted classes, rather than solely trusting the top-1 prediction. We show that Guided Zoom improves the classification accuracy of a deep convolutional neural network model and obtains state-of-the-art results on three fine-grained classification benchmark datasets.

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Sarah Adel Bargal, Andrea Zunino, Vitali Petsiuk, Jianming Zhang, Kate Saenko, Vittorio Murino, Stan Sclaroff. 2018-12-06. Guided Zoom: Questioning Network Evidence for Fine-grained Classification. https://arxiv.org/abs/1812.02626

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