Improving Object Detection with Selective Self-supervised Self-training

• URL: http://arxiv.org/abs/2007.09162v2
• Date: Fri, 24 Jul 2020 19:34:54 GMT
• Title: Improving Object Detection with Selective Self-supervised Self-training
• Authors: Yandong Li, Di Huang, Danfeng Qin, Liqiang Wang, Boqing Gong
• Abstract summary: We study how to leverage Web images to augment human-curated object detection datasets. We retrieve Web images by image-to-image search, which incurs less domain shift from the curated data than other search methods. We propose a novel learning method motivated by two parallel lines of work that explore unlabeled data for image classification.
• Score: 62.792445237541145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study how to leverage Web images to augment human-curated object detection datasets. Our approach is two-pronged. On the one hand, we retrieve Web images by image-to-image search, which incurs less domain shift from the curated data than other search methods. The Web images are diverse, supplying a wide variety of object poses, appearances, their interactions with the context, etc. On the other hand, we propose a novel learning method motivated by two parallel lines of work that explore unlabeled data for image classification: self-training and self-supervised learning. They fail to improve object detectors in their vanilla forms due to the domain gap between the Web images and curated datasets. To tackle this challenge, we propose a selective net to rectify the supervision signals in Web images. It not only identifies positive bounding boxes but also creates a safe zone for mining hard negative boxes. We report state-of-the-art results on detecting backpacks and chairs from everyday scenes, along with other challenging object classes.

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