Siamese DETR

Zeren Chen, Gengshi Huang, Wei Li, Jianing Teng, Kun Wang, Lu Sheng, Jing Shao, Chen Change Loy

Keywords: object detection, unsupervised learning, Transformer

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, especially the task-specific module Transformer. 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: (a) Multi-View Region Detection aims at learning to localize regions-of-interest between augmented views of the input, and (b) 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 will be made available.

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Please Choose The Closest Area That Your Submission Falls Into: Unsupervised and Self-supervised learning

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