This paper describes AutoFocus, an efficient multi-scale inference algorithm
for deep-learning based object detectors. Instead of processing an entire image
pyramid, AutoFocus adopts a coarse to fine approach and only processes regions
which are likely to contain small objects at finer scales. This is achieved by
predicting category agnostic segmentation maps for small objects at coarser
scales, called FocusPixels. FocusPixels can be predicted with high recall, and
in many cases, they only cover a small fraction of the entire image. To make
efficient use of FocusPixels, an algorithm is proposed which generates compact
rectangular FocusChips which enclose FocusPixels. The detector is only applied
inside FocusChips, which reduces computation while processing finer scales.
Different types of error can arise when detections from FocusChips of multiple
scales are combined, hence techniques to correct them are proposed. AutoFocus
obtains an mAP of 47.9% (68.3% at 50% overlap) on the COCO test-dev set while
processing 6.4 images per second on a Titan X (Pascal) GPU. This is 2.5X faster
than our multi-scale baseline detector and matches its mAP. The number of
pixels processed in the pyramid can be reduced by 5X with a 1% drop in mAP.
AutoFocus obtains more than 10% mAP gain compared to RetinaNet but runs at the
same speed with the same ResNet-101 backbone.

AutoFocus 是一种高效的基于深度学习的物体检测器的多尺度推理算法，只处理可能包含细节或小物体的区域，可以得到 47.9%（50% 重叠时的 68.3%）的 mAP，比 RetinaNet 多 10% 的 mAP。