demanding location information, we mainly work on the detection-based approach with special focus on incompletely-supervised learning.

Object Detection
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Nowadays, mainstream object detection algorithms have changed to CNN-based implementation due to its powerful representation and high accuracy. Such algorithms can be roughly categorized into two-stage object detector [@GirshickDDM14; @Girshick15; @RenHGS15], and one-stage object detector [@LiuAESRFB16; @RedmonF17]. Two-stage object detectors such as Faster RCNN [@RenHGS15] first extract region proposals and then perform classification and bounding box regression, while single-stage objectors such as YOLO [@RedmonF17] directly output the bounding box locations and the classification results without generating the region proposal. All the object detection algorithms by default following the fully supervised setting - requiring large amount of training images with annotated bounding boxes to achieve a high accuracy.

To reduce the annotation effort, weakly supervised learning trains object detector only based on image-level labels. Most approaches [@BilenV16; @CinbisVS17; @KantorovOCL16; @TangWBL17] first generates multiple region proposals for each image and then leverage multi-instance learning algorithms to solve the problem. However, such approaches still suffer from suboptimal accuracy due to the lack of instance-level labels.

In both the fully and weakly supervised settings, each image is either labeled with full bounding box annotations or 