JULAIN-Journal-Club.md

@@ -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>
+Paper: https://arxiv.org/abs/1908.07919<br>
+Code: https://paperswithcode.com/paper/deep-high-resolution-representation-learning-2
+
+
+* ~~17.12.2019 Christian Schiffer
    * Deep High-Resolution Representation Learning for Visual Recognition <br>
 https://arxiv.org/abs/1908.07919, Code: https://paperswithcode.com/paper/deep-high-resolution-representation-learning-2)<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>