Weakly- and Semi-supervised Faster R-CNN with Curriculum Learning
Jiasi Wang, Xinggang Wang, Wenyu Liu · 2018
Object detection is a core problem in computer vision and pattern recognition. In this paper, we study the problem of learning an effective object detector using weakly-annotated images (i.e., only the image level annotation is given) and a small proportion of fully-annotated images (i.e., bounding box level annotation is given) with curriculum learning. Our method is built upon Faster R-CNN. Different from previous weakly-supervised object detectors which rely on hand-craft object proposals, the proposed method learns a region proposal network using weakly- and semi-supervised training data. And the weakly-labeled images are fed into the deep network in a meaningful order which illustrates from easy to gradually more complex examples with curriculum learning. We name the Faster R-CNN trained using Weakly- And Semi-Supervised data with Curriculum Learning as WASSCL R-CNN. The WASSCL R-CNN is validated on the PASCAL VOC 2007 benchmark, and obtains 90% of a fully-supervised Faster R-CNN's performance (measured using mAP) with only 15% of fully-supervised annotations together with weak supervision. The results show that the proposed learning framework can significantly reduce the labeling efforts for obtaining reliable object detectors.