Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions

• URL: http://arxiv.org/abs/2305.18953v1
• Date: Tue, 30 May 2023 11:37:41 GMT
• Title: Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions
• Authors: Stefan Leitner, M. Jehanzeb Mirza, Wei Lin, Jakub Micorek, Marc Masana, Mateusz Kozinski, Horst Possegger, Horst Bischof
• Abstract summary: In autonomous driving scenarios, current object detection models show strong performance when tested in clear weather. We propose Domain-Incremental Learning through Activation Matching (DILAM) to adapt only the affine parameters of a clear weather pre-trained network to different weather conditions. Our memory bank is extremely lightweight, since affine parameters account for less than 2% of a typical object detector.
• Score: 16.014293219912
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In autonomous driving scenarios, current object detection models show strong performance when tested in clear weather. However, their performance deteriorates significantly when tested in degrading weather conditions. In addition, even when adapted to perform robustly in a sequence of different weather conditions, they are often unable to perform well in all of them and suffer from catastrophic forgetting. To efficiently mitigate forgetting, we propose Domain-Incremental Learning through Activation Matching (DILAM), which employs unsupervised feature alignment to adapt only the affine parameters of a clear weather pre-trained network to different weather conditions. We propose to store these affine parameters as a memory bank for each weather condition and plug-in their weather-specific parameters during driving (i.e. test time) when the respective weather conditions are encountered. Our memory bank is extremely lightweight, since affine parameters account for less than 2% of a typical object detector. Furthermore, contrary to previous domain-incremental learning approaches, we do not require the weather label when testing and propose to automatically infer the weather condition by a majority voting linear classifier.

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