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Large scale object detection datasets are constantly increasing their size interms of the number of classes and annotations count. Yet, the number ofobject-level categories annotated in detection datasets is an order ofmagnitude smaller than image-level classification labels. State-of-the artobject detection models are trained in a supervised fashion and this limits thenumber of object classes they can detect. In this paper, we propose a novelweight transfer network (WTN) to effectively and efficiently transfer knowledgefrom classification network's weights to detection network's weights to allowdetection of novel classes without box supervision. We first introduce inputand feature normalization schemes to curb the under-fitting during training ofa vanilla WTN. We then propose autoencoder-WTN (AE-WTN) which usesreconstruction loss to preserve classification network's information over allclasses in the target latent space to ensure generalization to novel classes.Compared to vanilla WTN, AE-WTN obtains absolute performance gains of 6% on twoOpen Images evaluation sets with 500 seen and 57 novel classes respectively,and 25% on a Visual Genome evaluation set with 200 novel classes. The code isavailable at https://github.com/xternalz/AE-WTN.

Scaling Object Detection by Transferring Classification Weights