5 citations · 5 across the 4 of their papers we have counts for
5 papers
Truncate-Split-Contrast: A Framework for Learning from Mislabeled Videos
Zixiao Wang, Junwu Weng, Chun Yuan +1
Learning with noisy label (LNL) is a classic problem that has been extensively studied for image tasks, but much less for video in the literature. A straightforward migration from…
Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration
Manyi Zhang, Yuxin Ren, Zihao Wang +1
Instance-dependent label noise is realistic but rather challenging, where the label-corruption process depends on instances directly. It causes a severe distribution shift between…
Improving the Latent Space of Image Style Transfer
Yunpeng Bai, Cairong Wang, Chun Yuan +2
Existing neural style transfer researches have studied to match statistical information between the deep features of content and style images, which were extracted by a pre-trained…
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…
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…