| Full name | SE-ResNeXt-SSMA |
| Description | SE-ResNeXt-101 backbone with multimodal SSMA fusion of visual RGB images (top-down views) and colorized depth maps as input to the network. |
| Publication title | Self-Supervised Model Adaptation for Multimodal Semantic Segmentation |
| Publication authors | Abhinav Valada, Rohit Mohan, Wolfram Burgard |
| Publication venue | arXiv |
| Publication URL | https://arxiv.org/abs/1808.03833 |
| Input Data Types | Uses Color,Uses Geometry Uses 2D |
| Programming language(s) | Python, Tensorflow |
| Hardware | Intel Xeon E5 CPU, NVIDIA TITAN X (Pascal) |
| Website | http://deepscene.cs.uni-freiburg.de |
| Submission creation date | 18 Jan, 2019 |
| Last edited | 20 Jan, 2019 |