| Full name | Self-Supervised Model Adaptation for Multimodal Semantic Segmentation |
| Description | Benchmarking the SSMA mutimodal semantic segmentation framework that dynamically adapts the fusion of modality-specific features while being sensitive to the object category, spatial location and scene context in a self-supervised manner. The model was trained with the visual RGB image and the HHA encoded depth image as input to the network. The architecture consists of two modality-specific encoder streams that fuse intermediate encoder representations into a single decoder using the SSMA fusion mechanism which optimally combines complementary features. |
| Publication title | Self-Supervised Model Adaptation for Multimodal Semantic Segmentation |
| Publication authors | Abhinav Valada, Rohit Mohan, Wolfram Burgard |
| Publication venue | International Journal of Computer Vision, 2019 |
| 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 |
| Source code or download URL | https://github.com/DeepSceneSeg/SSMA |
| Submission creation date | 29 Dec, 2018 |
| Last edited | 18 Jul, 2019 |
