Exploiting Diverse Characteristics and Adversarial Ambivalence for Domain Adaptive Segmentation
Authors: Bowen Cai, Huan Fu, Rongfei Jia, Binqiang Zhao, Hua Li, Yinghui Xu6850-6858
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method (DCAA) on various adaptation scenarios where the target images vary in weather conditions. The comparisons against baselines and the state-of-the-art approaches demonstrate the superiority of DCAA over the competitors. |
| Researcher Affiliation | Collaboration | Bowen Cai1,2, Huan Fu1, Rongfei Jia1, Binqiang Zhao1, Hua Li2 and Yinghui Xu1 1Alibaba Group 2Institute of Computing Technology, Chinese Academy of Sciences |
| Pseudocode | No | The paper describes its methods using textual explanations and figures, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | Our codes will be made public available. |
| Open Datasets | Yes | We evaluate our proposed approach on three challenging adaptation scenarios, i.e., GTA5 Cityscapes (Richter et al. 2016; Cordts et al. 2016; Sakaridis et al. 2018; Hu et al. 2019), SYNTHIA Cityscapes (Ros et al. 2016), and GTA5 BDD100K (Yu et al. 2020a). |
| Dataset Splits | Yes | Cityscapes-Cloudy provides 3,475 images with a resolution of 2048 1024, which are officially split into 2,975 training images and 500 validation images. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers in the main text, stating that implementation details are in the supplemental materials. |
| Experiment Setup | Yes | The full objective for our CGST can be expressed as: LCGST = Lc GAN + Lcls + λsc Lsc, (4) where the trade-off parameter λsc is set to 5.0 in our paper. In our paper, D is set to 256, and L = 19 is the number of semantic categories. We set λp to 0.6 to generate more pseudo-labels for target images benefiting from the adversarial ambivalence mechanism. |