# Deep Regionlets for Object Detection

arXiv: https://arxiv.org/abs/1712.02408 · HTML: https://mlsanity.com/paper/1712.02408/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2017.

## Built on by (most cited first, 6 of 6)

- [ASK: Adaptively Selecting Key Local Features for RGB-D Scene Recognition](https://mlsanity.com/paper/2110.07703/) (2021)
- [A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets](https://mlsanity.com/paper/2107.07927/) (2021)
- [The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions](https://mlsanity.com/paper/2101.07832/) (2021)
- [Lightweight Jet Reconstruction and Identification as an Object Detection Task](https://mlsanity.com/paper/2202.04499/) (2022)
- [Automated Optical Inspection of FAST's Reflector Surface using Drones and Computer Vision](https://mlsanity.com/paper/2212.09039/) (2022)
- [Deployment Prior Injection for Run-time Calibratable Object Detection](https://mlsanity.com/paper/2402.17207/) (2024)

## Abstract (arXiv)

In this paper, we propose a novel object detection framework named "Deep Regionlets" by establishing a bridge between deep neural networks and conventional detection schema for accurate generic object detection. Motivated by the abilities of regionlets for modeling object deformation 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 regions to learn the features from. The regionlet learning module focuses on local feature selection and transformation to alleviate local 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 soft regionlet selection and pooling. The Deep Regionlets framework is trained end-to-end without additional efforts. We perform ablation studies and conduct extensive experiments on the PASCAL VOC and Microsoft COCO datasets. The proposed framework outperforms state-of-the-art algorithms, such as RetinaNet and Mask R-CNN, even without additional segmentation labels.