Semi-Supervised Object Detection With Sparsely Annotated Dataset

When training an anchor-based object detector with a sparsely annotated dataset, the effort required to locate positive examples can cause performance degradation. Because anchor-based object detection models collect positive examples under IoU between anchors and ground-truth bounding boxes, in a sparsely annotated image, some objects that are not annotated can be assigned as negative examples, such as backgrounds. We attempt to solve this problem with two approaches: 1) using an anchor-less object detector and 2) using a single-object tracker for semi-supervised learning-based object detection. The proposed technique performs bidirectional single-object tracking from sparsely annotated bounding boxes as starting points in videos to obtain dense annotations. On applying our method to the EPIC-KITCHENS-55 dataset, we were able to achieve runner-up performance in the Unseen section, while achieving the first place in the Seen section of the EPIC-KITCHENS 2020 object detection challenge under IoU > 0.5 on the EPIC-KITCHENS 2020 object detection challenge.