MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving

• URL: http://arxiv.org/abs/2108.12178v1
• Date: Fri, 27 Aug 2021 08:47:01 GMT
• Title: MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving
• Authors: Kai Chen, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung
• Abstract summary: Self-supervised learning might be a promising way to improve model performance. Existing SSL methods usually rely on the single-centric-object guarantee. We propose Multi-instance Siamese Network (MultiSiam) to improve generalization ability and achieve state-of-the-art transfer performance.
• Score: 45.23708547617418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous driving has attracted much attention over the years but turns out to be harder than expected, probably due to the difficulty of labeled data collection for model training. Self-supervised learning (SSL), which leverages unlabeled data only for representation learning, might be a promising way to improve model performance. Existing SSL methods, however, usually rely on the single-centric-object guarantee, which may not be applicable for multi-instance datasets such as street scenes. To alleviate this limitation, we raise two issues to solve: (1) how to define positive samples for cross-view consistency and (2) how to measure similarity in multi-instance circumstances. We first adopt an IoU threshold during random cropping to transfer global-inconsistency to local-consistency. Then, we propose two feature alignment methods to enable 2D feature maps for multi-instance similarity measurement. Additionally, we adopt intra-image clustering with self-attention for further mining intra-image similarity and translation-invariance. Experiments show that, when pre-trained on Waymo dataset, our method called Multi-instance Siamese Network (MultiSiam) remarkably improves generalization ability and achieves state-of-the-art transfer performance on autonomous driving benchmarks, including Cityscapes and BDD100K, while existing SSL counterparts like MoCo, MoCo-v2, and BYOL show significant performance drop. By pre-training on SODA10M, a large-scale autonomous driving dataset, MultiSiam exceeds the ImageNet pre-trained MoCo-v2, demonstrating the potential of domain-specific pre-training. Code will be available at https://github.com/KaiChen1998/MultiSiam.

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