CPM R-CNN: Calibrating Point-guided Misalignment in Object Detection
arXiv:2003.03570
Abstract
In object detection, offset-guided and point-guided regression dominate anchor-based and anchor-free method separately. Recently, point-guided approach is introduced to anchor-based method. However, we observe points predicted by this way are misaligned with matched region of proposals and score of localization, causing a notable gap in performance. In this paper, we propose CPM R-CNN which contains three efficient modules to optimize anchor-based point-guided method. According to sufficient evaluations on the COCO dataset, CPM R-CNN is demonstrated efficient to improve the localization accuracy by calibrating mentioned misalignment. Compared with Faster R-CNN and Grid R-CNN based on ResNet-101 with FPN, our approach can substantially improve detection mAP by 3.3% and 1.5% respectively without whistles and bells. Moreover, our best model achieves improvement by a large margin to 49.9% on COCO test-dev. Code and models will be publicly available.
arXiv commentAccepted to WACV 2021
References in corpus (7)
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- R-FCN: Object Detection via Region-based Fully Convolutional Networks
- Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
- DSSD : Deconvolutional Single Shot Detector
- MMDetection: Open MMLab Detection Toolbox and Benchmark
- Cascade RetinaNet: Maintaining Consistency for Single-Stage Object Detection
- Grid R-CNN Plus: Faster and Better