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An Unsupervised Temporal Consistency (TC) Loss To Improve the Performance of Semantic Segmentation Networks.

, , , , , , , , , and . CVPR Workshops, page 12-20. Computer Vision Foundation / IEEE, (2021)

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From a Fourier-Domain Perspective on Adversarial Examples to a Wiener Filter Defense for Semantic Segmentation., , , , , , and . CoRR, (2020)A Self-Supervised Feature Map Augmentation (FMA) Loss and Combined Augmentations Finetuning to Efficiently Improve the Robustness of CNNs., , , , , , , , and . CSCS, page 3:1-3:8. ACM, (2020)Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety., , , , , , , , , and 31 other author(s). CoRR, (2021)An Unsupervised Temporal Consistency (TC) Loss To Improve the Performance of Semantic Segmentation Networks., , , , , , , , , and . CVPR Workshops, page 12-20. Computer Vision Foundation / IEEE, (2021)Robust Semantic Segmentation by Redundant Networks With a Layer-Specific Loss Contribution and Majority Vote., , , , , and . CVPR Workshops, page 1348-1358. Computer Vision Foundation / IEEE, (2020)Traffic Sign Classifiers Under Physical World Realistic Sticker Occlusions: A Cross Analysis Study., , , , , , and . IV, page 644-650. IEEE, (2022)Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates., , , , , and . IJCNN, page 1-8. IEEE, (2021)The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing., , , , , , and . IEEE Signal Process. Mag., 38 (1): 42-52 (2021)Unsupervised Temporal Consistency Metric for Video Segmentation in Highly-Automated Driving., , , , , , , , , and . CVPR Workshops, page 1369-1378. Computer Vision Foundation / IEEE, (2020)From a Fourier-Domain Perspective on Adversarial Examples to a Wiener Filter Defense for Semantic Segmentation., , , , , , and . IJCNN, page 1-8. IEEE, (2021)