Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions Article Swipe
Stefan Leitner
,
M. Jehanzeb Mirza
,
Wei Lin
,
Jakub Micorek
,
Marc Masana
,
Mateusz Koziński
,
Horst Possegger
,
Horst Bischof
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.18953
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.18953
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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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2305.18953
- https://arxiv.org/pdf/2305.18953
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378945438
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4378945438Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2305.18953Digital Object Identifier
- Title
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Sit Back and Relax: Learning to Drive Incrementally in All Weather ConditionsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-05-30Full publication date if available
- Authors
-
Stefan Leitner, M. Jehanzeb Mirza, Wei Lin, Jakub Micorek, Marc Masana, Mateusz Koziński, Horst Possegger, Horst BischofList of authors in order
- Landing page
-
https://arxiv.org/abs/2305.18953Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2305.18953Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2305.18953Direct OA link when available
- Concepts
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Forgetting, Computer science, Affine transformation, Artificial intelligence, Classifier (UML), Machine learning, Weather forecasting, Meteorology, Mathematics, Pure mathematics, Physics, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.forgetting. | 57 |
| abstract_inverted_index.performance | 10, 18 |
| abstract_inverted_index.pre-trained | 85 |
| abstract_inverted_index.Furthermore, | 142 |
| abstract_inverted_index.catastrophic | 56 |
| abstract_inverted_index.deteriorates | 19 |
| abstract_inverted_index.encountered. | 122 |
| abstract_inverted_index.lightweight, | 128 |
| abstract_inverted_index.unsupervised | 72 |
| abstract_inverted_index.automatically | 161 |
| abstract_inverted_index.significantly | 20 |
| abstract_inverted_index.weather-specific | 109 |
| abstract_inverted_index.Domain-Incremental | 64 |
| abstract_inverted_index.domain-incremental | 146 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.6700000166893005 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile |