# Cascade Region Proposal and Global Context for Deep Object Detection

arXiv: https://arxiv.org/abs/1710.10749 · HTML: https://mlsanity.com/paper/1710.10749/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2017.

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

- [SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering](https://mlsanity.com/paper/2204.02285/) (2022)
- [Recursively Refined R-CNN: Instance Segmentation with Self-RoI Rebalancing](https://mlsanity.com/paper/2104.01329/) (2021)
- [Context in object detection: a systematic literature review](https://mlsanity.com/paper/2503.23249/) (2025)
- [Modulating Localization and Classification for Harmonized Object Detection](https://mlsanity.com/paper/2103.08958/) (2021)
- [Colonoscopy polyp detection with massive endoscopic images](https://mlsanity.com/paper/2202.08730/) (2022)
- [Self-Balanced R-CNN for Instance Segmentation](https://mlsanity.com/paper/2404.16633/) (2024)

## Abstract (arXiv)

Deep region-based object detector consists of a region proposal step and a deep object recognition step. In this paper, we make significant improvements on both of the two steps. For region proposal we propose a novel lightweight cascade structure which can effectively improve RPN proposal quality. For object recognition we re-implement global context modeling with a few modications and obtain a performance boost (4.2% mAP gain on the ILSVRC 2016 validation set). Besides, we apply the idea of pre-training extensively and show its importance in both steps. Together with common training and testing tricks, we improve Faster R-CNN baseline by a large margin. In particular, we obtain 87.9% mAP on the PASCAL VOC 2012 test set, 65.3% on the ILSVRC 2016 test set and 36.8% on the COCO test-std set.