Learning Chained Deep Features and Classifiers for Cascade in Object Detection
By Wanli Ouyang, Kun Wang, Xin Zhu, Xiaogang Wang. 2017
20 citations recorded by the source corpus.
Cascade is a widely used approach that rejects obvious negative samples at early stages for learning better classifier and faster inference. This paper presents chained cascade network (CC-Net). In this CC-Net, the cascaded classifier at a stage is aided by the classification scores in previous stages. Feature chaining is further proposed so that the feature learning for the current cascade stage uses the features in previous stages as the prior information. The chained ConvNet features and classifiers of multiple stages are jointly learned in an end-to-end network. In this way, features and classifiers at latter stages handle more difficult samples with the help of features and classifiers in previous stages. It yields consistent boost in detection performance on benchmarks like PASCAL VOC 2007 and ImageNet. Combined with better region proposal, CC-Net leads to state-of-the-art result of 81.1% mAP on PASCAL VOC 2007.
Subfields: Computer Vision and Pattern Recognition, Media Technology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
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 Media Technology
Citations in the corpus
- Cascade R-CNN: High Quality Object Detection and Instance Segmentation (2019, background): “On the other hand, [34], [44] embedded the classic cascade architecture of [54] in an object detection network.”
- Cascade R-CNN: Delving Into High Quality Object Detection (2017, background): “[21, 28] embedded the classic cascade architecture of [34] in object detection networks.”