UP-DETR: Unsupervised Pre-training for Object Detection with Transformers

• URL: http://arxiv.org/abs/2011.09094v3
• Date: Mon, 24 Jul 2023 11:28:46 GMT
• Title: UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
• Authors: Zhigang Dai, Bolun Cai, Yugeng Lin, Junying Chen
• Abstract summary: We propose a novel pretext task named random query patch detection in Unsupervised Pre-training DETR (UP-DETR) Specifically, we randomly crop patches from the given image and then feed them as queries to the decoder. UP-DETR significantly boosts the performance of DETR with faster convergence and higher average precision on object detection, one-shot detection and panoptic segmentation.
• Score: 11.251593386108189
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: DEtection TRansformer (DETR) for object detection reaches competitive performance compared with Faster R-CNN via a transformer encoder-decoder architecture. However, trained with scratch transformers, DETR needs large-scale training data and an extreme long training schedule even on COCO dataset. Inspired by the great success of pre-training transformers in natural language processing, we propose a novel pretext task named random query patch detection in Unsupervised Pre-training DETR (UP-DETR). Specifically, we randomly crop patches from the given image and then feed them as queries to the decoder. The model is pre-trained to detect these query patches from the input image. During the pre-training, we address two critical issues: multi-task learning and multi-query localization. (1) To trade off classification and localization preferences in the pretext task, we find that freezing the CNN backbone is the prerequisite for the success of pre-training transformers. (2) To perform multi-query localization, we develop UP-DETR with multi-query patch detection with attention mask. Besides, UP-DETR also provides a unified perspective for fine-tuning object detection and one-shot detection tasks. In our experiments, UP-DETR significantly boosts the performance of DETR with faster convergence and higher average precision on object detection, one-shot detection and panoptic segmentation. Code and pre-training models: https://github.com/dddzg/up-detr.

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