Siamese DETR

• URL: http://arxiv.org/abs/2303.18144v1
• Date: Fri, 31 Mar 2023 15:29:25 GMT
• Title: Siamese DETR
• Authors: Zeren Chen, Gengshi Huang, Wei Li, Jianing Teng, Kun Wang, Jing Shao, Chen Change Loy, Lu Sheng
• Abstract summary: We present Siamese DETR, a self-supervised pretraining approach for the Transformer architecture in DETR. We consider learning view-invariant and detection-oriented representations simultaneously through two complementary tasks. The proposed Siamese DETR achieves state-of-the-art transfer performance on COCO and PASCAL VOC detection.
• Score: 87.45960774877798
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent self-supervised methods are mainly designed for representation learning with the base model, e.g., ResNets or ViTs. They cannot be easily transferred to DETR, with task-specific Transformer modules. In this work, we present Siamese DETR, a Siamese self-supervised pretraining approach for the Transformer architecture in DETR. We consider learning view-invariant and detection-oriented representations simultaneously through two complementary tasks, i.e., localization and discrimination, in a novel multi-view learning framework. Two self-supervised pretext tasks are designed: (i) Multi-View Region Detection aims at learning to localize regions-of-interest between augmented views of the input, and (ii) Multi-View Semantic Discrimination attempts to improve object-level discrimination for each region. The proposed Siamese DETR achieves state-of-the-art transfer performance on COCO and PASCAL VOC detection using different DETR variants in all setups. Code is available at https://github.com/Zx55/SiameseDETR.

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