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
The most similar approach to ours is the work based on global max pooling by Oquab @cite_28 . Instead of global pooling, they apply global pooling to localize a point on objects. However, their localization is limited to a point lying in the boundary of the object rather than determining the full extent of the object. We believe that while the and functions are rather similar, the use of average pooling encourages the network to identify the complete extent of the object. The basic intuition behind this is that the loss for average pooling benefits when the network identifies discriminative regions of an object as compared to max pooling. This is explained in greater detail and verified experimentally in Sec. . Furthermore, unlike @cite_28 , we demonstrate that this localization ability is generic and can be observed even for problems that the network was not trained on.
@cite_28
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Successful methods for visual object recognition typically rely on training datasets containing lots of richly annotated images. Detailed image annotation, e.g. by object bounding boxes, however, is both expensive and often subjective. We describe a weakly supervised convolutional neural network (CNN) for object classification that relies only on image-level labels, yet can learn from cluttered scenes containing multiple objects. We quantify its object classification and object location prediction performance on the Pascal VOC 2012 (20 object classes) and the much larger Microsoft COCO (80 object classes) datasets. We find that the network (i) outputs accurate image-level labels, (ii) predicts approximate locations (but not extents) of objects, and (iii) performs comparably to its fully-supervised counterparts using object bounding box annotation for training.