@@ -34,13 +34,25 @@ full paper and supplementary material available at https://hubert0527.github.io/
## Archive paper proposals
### Open
* 17.12.2019 Christian Schiffer
* 22.1.2020 Christian Schiffer<br>
**Full-resolution networks for semantic segmentation**<br>
Most common architectures for semantic segmentation consisting of an encoder and decoder part (e.g. U-Net) heavily reduce the spatial dimension of input images and may loose important details or fail to localize precisely. The proposed papers present full-resolution networks, which try to preserve high-resolution features throughout the network and improve localization accuracy.<br>
* Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes (CVPR'17 Oral)<br>
T. Pohlen, A. Hermans, M. Mathias, and B. Leibe<br>
Paper: http://arxiv.org/abs/1611.08323<br>
Code: https://github.com/TobyPDE/FRRN
* Deep High-Resolution Representation Learning for Visual Recognition, Wang et al.<br>
Human Pose Estimation+Semantic Segmentation+Object detection; SOTA Cityscapes Val, #3 best model PASCAL Context
* Multi-Scale Dense Networks for Resource Efficient Image Classification<br>
https://arxiv.org/abs/1703.09844)
* ==> updated by 22.1.2020~~
* 17.12.2019 Hanno Scharr
* A General and Adaptive Robust Loss Function<br>
Jonathan T. Barron, Google Research <br> http://openaccess.thecvf.com/content_CVPR_2019/papers/Barron_A_General_and_Adaptive_Robust_Loss_Function_CVPR_2019_paper.pdf<br>
