With the success of new computational architectures for visual processing, such as convolutional neural networks (CNN) and access to image databases with millions of labeled examples (e.g., ImageNet, Places), the state of the art in computer vision is advancing rapidly. One important factor for continued progress is to understand the representations that are learned by the inner layers of these deep architectures. Here we show that object detectors emerge from training CNNs to perform scene classification. As scenes are composed of objects, the CNN for scene classification automatically discovers meaningful objects detectors, representative of the learned scene categories. With object detectors emerging as a result of learning to recognize scenes, our work demonstrates that the same network can perform both scene recognition and object localization in a single forward-pass, without ever having explicitly learned the notion of objects.
Zhou et al. REF focused on scene images, and by visualizing the representation learned by each unit of the network, they showed that object detectors are implicitly learned.
Object Detectors Emerge in Deep Scene CNNs
ICLR 2015
arXiv: Computer Vision and Pattern Recognition
Computer Science