Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentation

By Philipp Oberdiek, Matthias Rottmann, Gernot A. Fink. 2020

32 citations recorded by the source corpus.

When deploying deep learning technology in self-driving cars, deep neural networks are constantly exposed to domain shifts. These include, e.g., changes in weather conditions, time of day, and long-term temporal shift. In this work we utilize a deep neural network trained on the Cityscapes dataset containing urban street scenes and infer images from a different dataset, the A2D2 dataset, containing also countryside and highway images. We present a novel pipeline for semantic segmenation that detects out-of-distribution (OOD) segments by means of the deep neural network’s prediction and performs image retrieval after feature extraction and dimensionality reduction on image patches. In our experiments we demonstrate that the deployed OOD approach is suitable for detecting out-of-distribution concepts. Furthermore, we evaluate the image patch retrieval qualitatively as well as quantitatively by means of the semi-compatible A2D2 ground truth and obtain mAP values of up to 52.2%.

Subfields: Building and Construction, Computer Vision and Pattern Recognition, Anomaly & Outlier Detection

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In Computer Vision and Pattern Recognition

Citations in the corpus

Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100 (2020, background): “The most obvious domain gaps stem from changes in locations [49], viewpoints [50], labels [51] and participants [52].”