Transfer Learning by Ranking for Weakly Supervised Object Annotation
This addresses the need for reducing manual annotation effort in object detection for computer vision applications, though it is incremental as it builds on existing weakly supervised approaches.
The paper tackles the problem of weakly supervised object annotation by framing it as a transfer learning task, using a novel learning-to-rank method to transfer models from auxiliary datasets to target datasets with unrelated categories, and reports outperforming state-of-the-art methods on the VOC dataset.
Most existing approaches to training object detectors rely on fully supervised learning, which requires the tedious manual annotation of object location in a training set. Recently there has been an increasing interest in developing weakly supervised approach to detector training where the object location is not manually annotated but automatically determined based on binary (weak) labels indicating if a training image contains the object. This is a challenging problem because each image can contain many candidate object locations which partially overlaps the object of interest. Existing approaches focus on how to best utilise the binary labels for object location annotation. In this paper we propose to solve this problem from a very different perspective by casting it as a transfer learning problem. Specifically, we formulate a novel transfer learning based on learning to rank, which effectively transfers a model for automatic annotation of object location from an auxiliary dataset to a target dataset with completely unrelated object categories. We show that our approach outperforms existing state-of-the-art weakly supervised approach to annotating objects in the challenging VOC dataset.