Unsupervised Domain Adaptation for Semantic Segmentation using Depth Distribution
Authors: Quanliang Wu, Huajun Liu
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on SYNTHIA-to-Cityscapes and SYNTHIAto-Mapillary benchmarks show the effectiveness of the method. |
| Researcher Affiliation | Academia | Quanliang Wu, Huajun Liu School of Computer Science Wuhan University, Wuhan, China {quanliangwu, huajunliu}@whu.edu.cn |
| Pseudocode | Yes | Algorithm 1: Spatial prior pseudo-labels refinement algorithm |
| Open Source Code | Yes | The source code is available at https://github.com/depdis/Depth_Distribution. |
| Open Datasets | Yes | For the SYNTHIA dataset [7] is used as the source domain. Following [12, 13], we use the SYNTHIA-RAND-CITYSCAPES split consisting of 9,400 synthetic images and their corresponding pixel-wise semantic labels and depth. For target domains, we use Cityscapes [8] and Mapillary Vistas [9] datasets. |
| Dataset Splits | Yes | Following [12, 13], we use the SYNTHIA-RAND-CITYSCAPES split consisting of 9,400 synthetic images and their corresponding pixel-wise semantic labels and depth. Similar to [26, 19, 12, 13], we report the performance of semantic segmentation based on mean Intersection over Union (m Io U in %) on the 16 classes of the Cityscapes validation set |
| Hardware Specification | Yes | All our experiments are conducted on a single NVIDIA 1080Ti GPU with a memory of 11GB. |
| Software Dependencies | No | The paper mentions "Our network is implemented on Py Torch [40]" but does not specify its version number or versions for other key software dependencies. |
| Experiment Setup | Yes | The learning rates of the prediction and discriminator networks are set as 2.5 10 4 and 1.0 10 3 respectively. In self-training, the parameters are: Q1 = 54K, Q2 = 30K. In our experiments, we use λseg = 1.0, λdep =0.5 10 2, λbal = 10 2, λtar = 5 10 2. |