  The prior self-supervised learning researches mainly select image-level
instance discrimination as pretext task. It achieves a fantastic classification
performance that is comparable to supervised learning methods. However, with
degraded transfer performance on downstream tasks such as object detection. To
bridge the performance gap, we propose a novel object-level self-supervised
learning method, called Contrastive learning with Downstream background
invariance (CoDo). The pretext task is converted to focus on instance location
modeling for various backgrounds, especially for downstream datasets. The
ability of background invariance is considered vital for object detection.
Firstly, a data augmentation strategy is proposed to paste the instances onto
background images, and then jitter the bounding box to involve background
information. Secondly, we implement architecture alignment between our
pretraining network and the mainstream detection pipelines. Thirdly,
hierarchical and multi views contrastive learning is designed to improve
performance of visual representation learning. Experiments on MSCOCO
demonstrate that the proposed CoDo with common backbones, ResNet50-FPN, yields
strong transfer learning results for object detection.
