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Confidence-Enhanced Mutual Knowledge for Uncertain Segmentation
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DOI：10.1109/TITS.2023.3309600.png)
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It is inevitable to recognize objects in adverse weather conditions where the uncertainty of contour areas is increased. Although some multi-task learning frameworks have gained from the directional supervision between boundary detection and semantic segmentation, the interaction between those two tasks is poorly investigated. Moreover, the performance of the contour detection is expected to degrade under foggy scenarios, because the auxiliary task also has no benefits from the main task. To address the potential risk in intelligent transportation systems, this paper proposes a mutual learning framework, named CE-MGN (Confidence-Enhanced Mutual Graph Network), to propagate confidence through continuous interaction between different tasks rather than only focusing on the accuracy of the main task. The CE-MGN performs an end-to-end training paradigm and jointly learns two tasks, contour detection and semantic segmentation, through pairwise confidence-enhancement mechanism. Moreover, the task interaction is converted into graph space to further relieve the information loss during the feature aggregation in Euclidean space. Such a framework is capable to improve the robustness of respective tasks because of the encouragement from its peer task. Extensive experiments show that our CE-MGN achieved mean IoU scores of 79.35% and 79.03% on CityScapes and Foggy CityScapes datasets, respectively. Besides, our models have a stable performance on different weather severity, where the performance fluctuation is less than 1%.
Keyword:
Adverse weather scenarios
semantic segmentation
pairwise confidence-enhancement
mutual learning
graph neural network
期刊
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8.4
论文数:
9.7K
被引数:
6.3W
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