Object detection is one of the major problems in computer vision, and has been extensively studied. Most of of the existing detection works rely on labor-intensive supervisions, such as ground truth bounding boxes of objects or at least image-level annotations. On the contrary, we propose an object detection method that does not require any form of supervisions on target tasks, by exploiting freely available web images. In order to facilitate effective knowledge transfer from web images, we introduce a multi-instance multi-label domain adaption learning framework with two key innovations. First of all, we propose an instance-level adversarial domain adaptation network with attention on foreground objects to transfer the object appearances from web domain to target domain. Second, to preserve the class-specific semantic structure of transferred object features, we propose a simultaneous transfer mechanism to transfer the supervision across domains through pseudo strong label generation. With our end-to-end framework that simultaneously learns a weakly supervised detector and transfers knowledge across domains, we achieved significant improvements over baseline methods on the benchmark datasets.

本文介绍了一种基于互联网图像的目标检测方法，使用了多实例、多标签和迁移学习的框架，并通过实例级别的对抗性域适应网络和同时转移的机制转移知识，实现了在基准数据集上比基线方法显著提升的弱监督检测器。

基于网络知识迁移的零注释目标检测