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Cited by 584

The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.

Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes

Open access
Pohlen, Tobias, Hermans, Alexander, Mathias, Markus, Leibe, Bastian·2017·2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr.2017.353
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Abstract

Semantic image segmentation is an essential component of modern autonomous driving systems, as an accurate understanding of the surrounding scene is crucial to navigation and action planning. Current state-of-the-art approaches in semantic image segmentation rely on pre-trained networks that were initially developed for classifying images as a whole. While these networks exhibit outstanding recognition performance (i.e., what is visible?), they lack localization accuracy (i.e., where precisely is something located?). Therefore, additional processing steps have to be performed in order to obtain pixel-accurate segmentation masks at the full image resolution. To alleviate this problem we propose a novel ResNet-like architecture that exhibits strong localization and recognition performance. We combine multi-scale context with pixel-level accuracy by using two processing streams within our network: One stream carries information at the full image resolution, enabling precise adherence to segment boundaries. The other stream undergoes a sequence of pooling operations to obtain robust features for recognition. The two streams are coupled at the full image resolution using residuals. Without additional processing steps and without pre-training, our approach achieves an intersection-over-union score of 71.8% on the Cityscapes dataset.

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Metadata

Authors
Pohlen, T., Hermans, A., Mathias, M., & Leibe, B.
Venue
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2017-07
Pages
3309-3318
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2017.353
OpenAlex
W2557406251
Cited by
584
References
20
FWCI
23.38
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes. https://app.cochat.ai/papers/10.1109/cvpr.2017.353
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Pohlen, T., Hermans, A., Mathias, M., & Leibe, B. (2017). Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.353

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