This paper proposes a deep learning architecture based on Residual Network that
dynamically adjusts the number of executed layers for the regions of the image.
This architecture is end-to-end trainable, deterministic and problem-agnostic. It
is therefore applicable without any modifications to a wide range of computer
vision problems such as image classification, object detection and image
segmentation. We present experimental results showing that this model improves the
computational efficiency of ResNet on the challenging ImageNet classification and
COCO object detection datasets. Additionally, we evaluate the computation time maps
on the image saliency dataset cat2000 and find that they correlate surprisingly
well with human eye fixation positions.