Aligning Pretraining for Detection via Object-Level Contrastive Learning

• URL: http://arxiv.org/abs/2106.02637v1
• Date: Fri, 4 Jun 2021 17:59:52 GMT
• Title: Aligning Pretraining for Detection via Object-Level Contrastive Learning
• Authors: Fangyun Wei, Yue Gao, Zhirong Wu, Han Hu, Stephen Lin
• Abstract summary: Image-level contrastive representation learning has proven to be highly effective as a generic model for transfer learning. We argue that this could be sub-optimal and thus advocate a design principle which encourages alignment between the self-supervised pretext task and the downstream task. Our method, called Selective Object COntrastive learning (SoCo), achieves state-of-the-art results for transfer performance on COCO detection.
• Score: 57.845286545603415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image-level contrastive representation learning has proven to be highly effective as a generic model for transfer learning. Such generality for transfer learning, however, sacrifices specificity if we are interested in a certain downstream task. We argue that this could be sub-optimal and thus advocate a design principle which encourages alignment between the self-supervised pretext task and the downstream task. In this paper, we follow this principle with a pretraining method specifically designed for the task of object detection. We attain alignment in the following three aspects: 1) object-level representations are introduced via selective search bounding boxes as object proposals; 2) the pretraining network architecture incorporates the same dedicated modules used in the detection pipeline (e.g. FPN); 3) the pretraining is equipped with object detection properties such as object-level translation invariance and scale invariance. Our method, called Selective Object COntrastive learning (SoCo), achieves state-of-the-art results for transfer performance on COCO detection using a Mask R-CNN framework. Code and models will be made available.

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