  An increasing number of applications in computer vision, specially, in
medical imaging and remote sensing, become challenging when the goal is to
classify very large images with tiny informative objects. Specifically, these
classification tasks face two key challenges: $i$) the size of the input image
is usually in the order of mega- or giga-pixels, however, existing deep
architectures do not easily operate on such big images due to memory
constraints, consequently, we seek a memory-efficient method to process these
images; and $ii$) only a very small fraction of the input images are
informative of the label of interest, resulting in low region of interest (ROI)
to image ratio. However, most of the current convolutional neural networks
(CNNs) are designed for image classification datasets that have relatively
large ROIs and small image sizes (sub-megapixel). Existing approaches have
addressed these two challenges in isolation. We present an end-to-end CNN model
termed Zoom-In network that leverages hierarchical attention sampling for
classification of large images with tiny objects using a single GPU. We
evaluate our method on four large-image histopathology, road-scene and
satellite imaging datasets, and one gigapixel pathology dataset. Experimental
results show that our model achieves higher accuracy than existing methods
while requiring less memory resources.
