R FCN 3000 at 30fps: Decoupling Detection and Classification
We propose a modular approach towards large scale real time object detection by decoupling objectness detection and classification. We exploit the fact that many object classes are visually similar and share parts. Thus, a universal objectness detector can be learned for class agnostic object detection followed by fine grained classification using a (non)linear classifier. Our approach is a modification of the R FCN architecture to learn shared filters for performing localization across different object classes. We trained a detector for 3000 object classes, called R FCN 3000, that obtains an mAP of 34.9% on the ImageNet detection dataset. It outperforms YOLO 9000 by 18% while processing 30 images per second. We also show that the objectness learned by R FCN 3000 generalizes to novel classes and the performance increases with the number of training object classes supporting the hypothesis that it is possible to learn a universal objectness detector. Code will be made available.
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition