SGAT4PASS: Spherical Geometry-Aware Transformer for PAnoramic Semantic Segmentation
Authors: Xuewei Li, Tao Wu, Zhongang Qi, Gaoang Wang, Ying Shan, Xi Li
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on Stanford2D3D Panoramic datasets show that SGAT4PASS significantly improves performance and robustness, with approximately a 2% increase in m Io U, and when small 3D disturbances occur in the data, the stability of our performance is improved by an order of magnitude. |
| Researcher Affiliation | Collaboration | 1College of Computer Science and Technology, Zhejiang University 2ARC Lab, Tencent PCG 3Zhejiang University-University of Illinois at Urbana-Champaign Institute, Zhejiang University 4Zhejiang Singapore Innovation and AI Joint Research Lab, Hangzhou |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and supplementary material are available at https: //github.com/Tencent ARC/SGAT4PASS. |
| Open Datasets | Yes | We validate SGAT4PASS on Stanford2D3D Panoramic datasets [Armeni et al., 2017]. |
| Dataset Splits | Yes | It has 1,413 panoramas, and 13 semantic classes are labeled, and has 3 official folds, fold 1 / 2 / 3. We follow the report style of previous work [Zhang et al., 2022a] [Zhang et al., 2022b]. |
| Hardware Specification | Yes | Our experiments are conducted with a server with four A100 GPUs. |
| Software Dependencies | No | The paper mentions using 'Adam W' as an optimizer but does not specify version numbers for other software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We use Trans4PASS+ [Zhang et al., 2022b] as our baseline and set an initial learning rate of 8e-5, which is scheduled by the poly strategy with 0.9 power over 150 epochs. The optimizer is Adam W [Kingma and Ba, 2015] with epsilon 1e-8, weight decay 1e-4, and batch size is 4 on each GPU. Other settings and hyperparameters are set the same as Trans4PASS+ [Zhang et al., 2022b]. |