most citedA Self-Supervised Feature Map Augmentation (FMA) Loss and Combined Augmentations Finetuning to Efficiently Improve the Robustness of CNNs

5 citations · 5 across the 1 of their papers we have counts for

collaborators

2 papers

cs.CV2020★ 5 cited

A Self-Supervised Feature Map Augmentation (FMA) Loss and Combined Augmentations Finetuning to Efficiently Improve the Robustness of CNNs

Nikhil Kapoor, Chun Yuan, Jonas Löhdefink +6

Deep neural networks are often not robust to semantically-irrelevant changes in the input. In this work we address the issue of robustness of state-of-the-art deep convolutional ne…

cs.CV2020

Reducing the Annotation Effort for Video Object Segmentation Datasets

Paul Voigtlaender, Lishu Luo, Chun Yuan +2

For further progress in video object segmentation (VOS), larger, more diverse, and more challenging datasets will be necessary. However, densely labeling every frame with pixel mas…