  In this paper, we propose a novel object detection algorithm named "Deep
Regionlets" by integrating deep neural networks and a conventional detection
schema for accurate generic object detection. Motivated by the effectiveness of
regionlets for modeling object deformations and multiple aspect ratios, we
incorporate regionlets into an end-to-end trainable deep learning framework.
The deep regionlets framework consists of a region selection network and a deep
regionlet learning module. Specifically, given a detection bounding box
proposal, the region selection network provides guidance on where to select
sub-regions from which features can be learned from. An object proposal
typically contains 3-16 sub-regions. The regionlet learning module focuses on
local feature selection and transformations to alleviate the effects of
appearance variations. To this end, we first realize non-rectangular region
selection within the detection framework to accommodate variations in object
appearance. Moreover, we design a "gating network" within the regionlet leaning
module to enable instance dependent soft feature selection and pooling. The
Deep Regionlets framework is trained end-to-end without additional efforts. We
present ablation studies and extensive experiments on the PASCAL VOC dataset
and the Microsoft COCO dataset. The proposed method yields competitive
performance over state-of-the-art algorithms, such as RetinaNet and Mask R-CNN,
even without additional segmentation labels.
