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..
Rethinking Alignment and Uniformity in Unsupervised Image Semantic Segmentation
Authors: Daoan Zhang, Chenming Li, Haoquan Li, Wenjian Huang, Lingyun Huang, Jianguo Zhang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on multiple semantic segmentation benchmarks show that our unsupervised segmentation framework specializes in catching semantic representations, which outperforms all the unpretrained and even several pretrained methods.Ablation Study Pixel-wise Encoder and Semantic-wise Generator. We first tested the effects of different backbones on the pixel-wise encoder and semantic-wise generator. The results are presented in Table 4. |
| Researcher Affiliation | Collaboration | Daoan Zhang1,3, Chenming Li1, Haoquan Li1, Wenjian Huang1, Lingyun Huang3, Jianguo Zhang1, 2* 1Southern University of Science and Technology 2Peng Cheng Laboratory 3Ping An Technology (Shenzhen) Co., Ltd. |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block is present in the paper. |
| Open Source Code | No | No statement explicitly providing access to source code for the described methodology or a link to a code repository is found in the paper. |
| Open Datasets | Yes | The experiments are conducted on five semantic segmentation benchmarks of different scenarios, including COCOStuff-3(Caesar, Uijlings, and Ferrari 2018), COCO-Stuff-15, COCO-Stuff-27, Cityscapes(Cordts et al. 2016) and POTSDAM(Rottensteiner et al. 2014) with various segmentation targets. |
| Dataset Splits | Yes | We also apply the widely used Cityscapes dataset, which contains 5,000 images focusing on street scenes, divided into 2,975 and 500 images used for training and validation. |
| Hardware Specification | Yes | All datasets are trained for 30 epochs on 4 Tesla V100 32G GPUs with Adam optimizer. |
| Software Dependencies | No | All datasets are trained for 30 epochs on 4 Tesla V100 32G GPUs with Adam optimizer. The paper mentions the optimizer but does not specify software dependencies like programming languages or deep learning frameworks with version numbers. |
| Experiment Setup | Yes | All datasets are trained for 30 epochs on 4 Tesla V100 32G GPUs with Adam optimizer. The learning rate is set to 1e 4. |