Large scale object detection datasets are constantly increasing their size in terms of the number of classes and annotations count. Yet, the number of object-level categories annotated in detection datasets is an order of magnitude smaller than image-level classification labels. State-of-the art object detection models are trained in a supervised fashion and this limits the number of object classes they can detect. In this paper, we propose a novel weight transfer network (WTN) to effectively and efficiently transfer knowledge from classification network's weights to detection network's weights to allow detection of novel classes without box supervision. We first introduce input and feature normalization schemes to curb the under-fitting during training of a vanilla WTN. We then propose autoencoder-WTN (AE-WTN) which uses reconstruction loss to preserve classification network's information over all classes in the target latent space to ensure generalization to novel classes. Compared to vanilla WTN, AE-WTN obtains absolute performance gains of 6% on two Open 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 is available at https://github.com/xternalz/AE-WTN.

本文提出了一种名为权重传输网络（WTN）的新方法，通过权重的有效和高效传输，可以在不需要标注的情况下检测出新颖物体类别。相比基础的WTN，引入自编码-WTN（AE-WTN）方法可以在两个含有500和57种新颖类别的Open Images数据集以及一个含有200种新颖类别的Visual Genome数据集中获得6％和25％的绝对性能提升。

通过转移分类权重来扩展物体检测