UGainS: Uncertainty Guided Anomaly Instance Segmentation Article Swipe
A. N. Nekrasov
,
Alexander Hermans
,
Lars Kuhnert
,
Bastian Leibe
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.02046
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.02046
A single unexpected object on the road can cause an accident or may lead to injuries. To prevent this, we need a reliable mechanism for finding anomalous objects on the road. This task, called anomaly segmentation, can be a stepping stone to safe and reliable autonomous driving. Current approaches tackle anomaly segmentation by assigning an anomaly score to each pixel and by grouping anomalous regions using simple heuristics. However, pixel grouping is a limiting factor when it comes to evaluating the segmentation performance of individual anomalous objects. To address the issue of grouping multiple anomaly instances into one, we propose an approach that produces accurate anomaly instance masks. Our approach centers on an out-of-distribution segmentation model for identifying uncertain regions and a strong generalist segmentation model for anomaly instances segmentation. We investigate ways to use uncertain regions to guide such a segmentation model to perform segmentation of anomalous instances. By incorporating strong object priors from a generalist model we additionally improve the per-pixel anomaly segmentation performance. Our approach outperforms current pixel-level anomaly segmentation methods, achieving an AP of 80.08% and 88.98% on the Fishyscapes Lost and Found and the RoadAnomaly validation sets respectively. Project page: https://vision.rwth-aachen.de/ugains
Related Topics
Concepts
Segmentation
Anomaly detection
Anomaly (physics)
Artificial intelligence
Computer science
Heuristics
Object (grammar)
Scale-space segmentation
Pixel
Segmentation-based object categorization
Pattern recognition (psychology)
Image segmentation
Computer vision
Physics
Condensed matter physics
Operating system
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2308.02046
- https://arxiv.org/pdf/2308.02046
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385644078
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385644078Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2308.02046Digital Object Identifier
- Title
-
UGainS: Uncertainty Guided Anomaly Instance SegmentationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-03Full publication date if available
- Authors
-
A. N. Nekrasov, Alexander Hermans, Lars Kuhnert, Bastian LeibeList of authors in order
- Landing page
-
https://arxiv.org/abs/2308.02046Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2308.02046Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2308.02046Direct OA link when available
- Concepts
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Segmentation, Anomaly detection, Anomaly (physics), Artificial intelligence, Computer science, Heuristics, Object (grammar), Scale-space segmentation, Pixel, Segmentation-based object categorization, Pattern recognition (psychology), Image segmentation, Computer vision, Physics, Condensed matter physics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.assigning | 53 |
| abstract_inverted_index.injuries. | 15 |
| abstract_inverted_index.instances | 95, 128 |
| abstract_inverted_index.mechanism | 23 |
| abstract_inverted_index.per-pixel | 162 |
| abstract_inverted_index.uncertain | 118, 135 |
| abstract_inverted_index.approaches | 48 |
| abstract_inverted_index.autonomous | 45 |
| abstract_inverted_index.evaluating | 79 |
| abstract_inverted_index.generalist | 123, 156 |
| abstract_inverted_index.individual | 84 |
| abstract_inverted_index.instances. | 148 |
| abstract_inverted_index.unexpected | 2 |
| abstract_inverted_index.validation | 190 |
| abstract_inverted_index.Fishyscapes | 183 |
| abstract_inverted_index.RoadAnomaly | 189 |
| abstract_inverted_index.heuristics. | 67 |
| abstract_inverted_index.identifying | 117 |
| abstract_inverted_index.investigate | 131 |
| abstract_inverted_index.outperforms | 168 |
| abstract_inverted_index.performance | 82 |
| abstract_inverted_index.pixel-level | 170 |
| abstract_inverted_index.additionally | 159 |
| abstract_inverted_index.performance. | 165 |
| abstract_inverted_index.segmentation | 51, 81, 114, 124, 141, 145, 164, 172 |
| abstract_inverted_index.incorporating | 150 |
| abstract_inverted_index.respectively. | 192 |
| abstract_inverted_index.segmentation, | 35 |
| abstract_inverted_index.segmentation. | 129 |
| abstract_inverted_index.out-of-distribution | 113 |
| abstract_inverted_index.https://vision.rwth-aachen.de/ugains | 195 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile |