Rethinking Alignment and Uniformity in Unsupervised Image Semantic Segmentation
Authors: Daoan Zhang, Chenming Li, Haoquan Li, Wenjian Huang, Lingyun Huang, Jianguo Zhang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | 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. |