Edge-Aware Guidance Fusion Network for RGB–Thermal Scene Parsing
Authors: Wujie Zhou, Shaohua Dong, Caie Xu, Yaguan Qian3571-3579
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments were performed on benchmark datasets to demonstrate the effectiveness of the proposed EGFNet and its superior performance compared with state-of-the-art methods. The code and results can be found at https://github.com/Shaohua Dong2021/EGFNet. |
| Researcher Affiliation | Academia | Wujie Zhou*, Shaohua Dong, Caie Xu, Yaguan Qian School of Information and Electronic Engineering, Zhejiang University of Science & Technology, Hangzhou, China. wujiezhou@163.com, shaohuadong2021@126.com, caiexu@163.com, qianyaguan@zust.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and results can be found at https://github.com/Shaohua Dong2021/EGFNet. |
| Open Datasets | Yes | We trained the EGFNet on the MFNet (Ha et al. 2017) and PST900 (Shivakumar et al. 2020) datasets. |
| Dataset Splits | Yes | We followed the training, testing, verification, and dataset splitting approaches used by (Ha et al. 2017). The PST900 dataset contains 894 aligned pairs of RGB and thermal images with pixel-level human annotations comprising five semantic classes, including the background. We used the splitting approach proposed by (Shivakumar et al. 2020) and resized each input image to 640 1280 pixels. |
| Hardware Specification | Yes | A computer equipped with an Intel 3.6 GHz i7 CPU and a single NVIDIA TITAN Xp graphics card was used for training and testing. |
| Software Dependencies | Yes | We used the Py Torch 1.7.0, CUDA 10.0, and cu DNN 7.6 libraries to implement the proposed EGFNet. |
| Experiment Setup | Yes | We trained EGFNet for 400 epochs and used the Ranger optimizer with an initial learning rate and weight decay of 5e-5 and 5e-4, respectively. We also used the weighted cross-entropy for both the semantic and boundary loss functions as well as weighting detailed by (Paszke et al. 2016). |