  We present an efficient foveal framework to perform object detection. A scale
normalized image pyramid (SNIP) is generated that, like human vision, only
attends to objects within a fixed size range at different scales. Such a
restriction of objects' size during training affords better learning of
object-sensitive filters, and therefore, results in better accuracy. However,
the use of an image pyramid increases the computational cost. Hence, we propose
an efficient spatial sub-sampling scheme which only operates on fixed-size
sub-regions likely to contain objects (as object locations are known during
training). The resulting approach, referred to as Scale Normalized Image
Pyramid with Efficient Resampling or SNIPER, yields up to 3 times speed-up
during training. Unfortunately, as object locations are unknown during
inference, the entire image pyramid still needs processing. To this end, we
adopt a coarse-to-fine approach, and predict the locations and extent of
object-like regions which will be processed in successive scales of the image
pyramid. Intuitively, it's akin to our active human-vision that first skims
over the field-of-view to spot interesting regions for further processing and
only recognizes objects at the right resolution. The resulting algorithm is
referred to as AutoFocus and results in a 2.5-5 times speed-up during inference
when used with SNIP.
