Learning Region Features for Object Detection
arXiv:1803.07066
Abstract
While most steps in the modern object detection methods are learnable, the region feature extraction step remains largely hand-crafted, featured by RoI pooling methods. This work proposes a general viewpoint that unifies existing region feature extraction methods and a novel method that is end-to-end learnable. The proposed method removes most heuristic choices and outperforms its RoI pooling counterparts. It moves further towards fully learnable object detection.
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
- Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- Deformable Convolutional Networks
- Scalable, High-Quality Object Detection
- Light-Head R-CNN: In Defense of Two-Stage Object Detector
- Learning non-maximum suppression