Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |