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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-regiondoi:10.1109/cvpr.2018.00435 dblp:conf/cvpr/ZhaiF0L18 fatcat:7xtlg57kzrd23pkbzhwhfsfuxy