Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations

CVPR 2019 Wonhee Lee Joonil Na Gunhee Kim

In spite of recent enormous success of deep convolutional networks in object detection, they require a large amount of bounding box annotations, which are often time-consuming and error-prone to obtain. To make better use of given limited labels, we propose a novel object detection approach that takes advantage of both multi-task learning (MTL) and self-supervised learning (SSL)... (read more)

PDF Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper