The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
| Autor: | Paul-Edouard Sarlin, Cesar Cadena, Roland Siegwart, Juan Nieto, Hermann Blum |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Anomaly detection Uncertainty estimation Bayesian inference Machine learning computer.software_genre Artificial Intelligence Segmentation Out-of-distribution detection business.industry Deep learning Density estimation Semantic segmentation Autonomous driving Pattern recognition (psychology) Softmax function Benchmark (computing) Computer Vision and Pattern Recognition Artificial intelligence business computer Software |
| Zdroj: | International Journal of Computer Vision, 129 (11) |
| ISSN: | 0920-5691 1573-1405 |
| DOI: | 10.48550/arxiv.1904.03215 |
| Popis: | Deep learning has enabled impressive progress in the accuracy of semantic segmentation. Yet, the ability to estimate uncertainty and detect failure is key for safety-critical applications like autonomous driving. Existing uncertainty estimates have mostly been evaluated on simple tasks, and it is unclear whether these methods generalize to more complex scenarios. We present Fishyscapes, the first public benchmark for anomaly detection in a real-world task of semantic segmentation for urban driving. It evaluates pixel-wise uncertainty estimates towards the detection of anomalous objects. We adapt state-of-the-art methods to recent semantic segmentation models and compare uncertainty estimation approaches based on softmax confidence, Bayesian learning, density estimation, image resynthesis, as well as supervised anomaly detection methods. Our results show that anomaly detection is far from solved even for ordinary situations, while our benchmark allows measuring advancements beyond the state-of-the-art. Results, data and submission information can be found at https://fishyscapes.com/. International Journal of Computer Vision, 129 (11) ISSN:0920-5691 ISSN:1573-1405 |
| Databáze: | OpenAIRE |
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