5 research outputs found
Feature Selective Networks for Object Detection
Objects for detection usually have distinct characteristics in different
sub-regions and different aspect ratios. However, in prevalent two-stage object
detection methods, Region-of-Interest (RoI) features are extracted by RoI
pooling with little emphasis on these translation-variant feature components.
We present feature selective networks to reform the feature representations of
RoIs by exploiting their disparities among sub-regions and aspect ratios. Our
network produces the sub-region attention bank and aspect ratio attention bank
for the whole image. The RoI-based sub-region attention map and aspect ratio
attention map are selectively pooled from the banks, and then used to refine
the original RoI features for RoI classification. Equipped with a light-weight
detection subnetwork, our network gets a consistent boost in detection
performance based on general ConvNet backbones (ResNet-101, GoogLeNet and
VGG-16). Without bells and whistles, our detectors equipped with ResNet-101
achieve more than 3% mAP improvement compared to counterparts on PASCAL VOC
2007, PASCAL VOC 2012 and MS COCO datasets