Аннотация
Most object detectors contain two important components: a feature extractor
and an object classifier. The feature extractor has rapidly evolved with
significant research efforts leading to better deep convolutional
architectures. The object classifier, however, has not received much attention
and many recent systems (like SPPnet and Fast/Faster R-CNN) use simple
multi-layer perceptrons. This paper demonstrates that carefully designing deep
networks for object classification is just as important. We experiment with
region-wise classifier networks that use shared, region-independent
convolutional features. We call them "Networks on Convolutional feature maps"
(NoCs). We discover that aside from deep feature maps, a deep and convolutional
per-region classifier is of particular importance for object detection, whereas
latest superior image classification models (such as ResNets and GoogLeNets) do
not directly lead to good detection accuracy without using such a per-region
classifier. We show by experiments that despite the effective ResNets and
Faster R-CNN systems, the design of NoCs is an essential element for the
1st-place winning entries in ImageNet and MS COCO challenges 2015.
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