arXiv:1512.04150cs.CV
Learning Deep Features for Discriminative Localization
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Abstract
In this work, we revisit the global average pooling layer proposed in [13], and shed light on how it explicitly enables the convolutional neural network to have remarkable localization ability despite being trained on image-level labels. While this technique was previously proposed as a means for regularizing training, we find that it actually builds a generic localizable deep representation that can be applied to a variety of tasks. Despite the apparent simplicity of global average pooling, we are able to achieve 37.1% top-5 error for object localization on ILSVRC 2014, which is remarkably close to the 34.2% top-5 error achieved by a fully supervised CNN approach. We demonstrate that our network is able to localize the discriminative image regions on a variety of tasks despite not being trained for them
It's based on this paper https://arxiv.org/pdf/1512.04150.pdf The approach is different than the Faster R-CNN etc mentioned below as it is not fully supervised, i.e. the position of the objected is not in the training data. I can write a simple tutorial and link in the article…
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