Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection
By Xiongwei Wu, Daoxin Zhang, Jianke Zhu, Steven C. H. Hoi. 2018
34 citations recorded by the source corpus.
Recent years have witnessed many exciting achievements for object detection using deep learning techniques. Despite achieving significant progresses, most existing detectors are designed to detect objects with relatively low-quality prediction of locations, i.e., often trained with the threshold of Intersection over Union (IoU) set to 0.5 by default, which can yield low-quality or even noisy detections. It remains an open challenge for how to devise and train a high-quality detector that can achieve more precise localization (i.e., IoU$>$0.5) without sacrificing the detection performance. In this paper, we propose a novel single-shot detection framework of Bidirectional Pyramid Networks (BPN) towards high-quality object detection, which consists of two novel components: (i) a Bidirectional Feature Pyramid structure for more effective and robust feature representations; and (ii) a Cascade Anchor Refinement to gradually refine the quality of predesigned anchors for more effective training. Our experiments showed that the proposed BPN achieves the best performances among all the single-stage object detectors on both PASCAL VOC and MS COCO datasets, especially for high-quality detections.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Zhaowei Cai
- Nuno Vasconcelos
- Jiashi Feng
- Chen Change Loy
- Alan L. Yuille
- Lu Yuan
- Jianfeng Gao
- Philip H. S. Torr
In Computer Vision and Pattern Recognition
- Zhaowei Cai
- Nuno Vasconcelos
- Luc Van Gool
- Xiaogang Wang
- Dacheng Tao
- Alan L. Yuille
- Andrew Zisserman
- Jiashi Feng
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Cascade R-CNN: High Quality Object Detection and Instance Segmentation (2019, methodology): “[40], [54] applied it to single-shot object detectors, showing nontrivial improvements for high quality single-shot detection, for general objects and pedestrians, respectively.”
- Region Proposal by Guided Anchoring (2019, neutral): “(iv) Some single-shot detectors [33, 30] refine anchors by multiple regression and classification.”