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..
GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models
Authors: Chen Liang, Wenguan Wang, Jiaxu Miao, Yi Yang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With a variety of segmentation architectures and backbones, GMMSeg outperforms the discriminative counterparts on three closed-set datasets. More impressively, without any modification, GMMSeg even performs well on open-world datasets. We respectively examine the efficacy and robustness of GMMSeg on semantic segmentation ( 4.1) and anomaly segmentation ( 4.2). In 4.3, we provide diagnostic analysis on our core model design. |
| Researcher Affiliation | Collaboration | Chen Liang1,3 , Wenguan Wang2 , Jiaxu Miao1, Yi Yang1 1CCAI, Zhejiang University 2Re LER, AAII, University of Technology Sydney 3Baidu Research |
| Pseudocode | No | The paper includes equations and figures but does not contain any clearly labeled pseudocode or algorithm blocks. Procedural steps are described within the text or through equations. |
| Open Source Code | Yes | https://github.com/leonnnop/GMMSeg. We promise code and instructions shall be made publicly available right after acceptance. |
| Open Datasets | Yes | We conduct experiments on three widely used semantic segmentation datasets: ADE20K [53] has 20K/2K/3K images in train/val/test set, with 150 stuff/object categories in total. Cityscapes [54] has 2,975/500/1,524 fine-labeled images for train/val/test set with 19 classes. COCO-Stuff [55] has 10K images (9K/1K for train/test), pixel-wise labeled with 171 classes. |
| Dataset Splits | Yes | ADE20K [53] has 20K/2K/3K images in train/val/test set, with 150 stuff/object categories in total. Cityscapes [54] has 2,975/500/1,524 fine-labeled images for train/val/test set with 19 classes. COCO-Stuff [55] has 10K images (9K/1K for train/test), pixel-wise labeled with 171 classes. |
| Hardware Specification | Yes | using 8/16 NVIDIA Tesla A100 GPUs. We measure the fps on a single NVIDIA Ge Force RTX 3090 GPU with a batch size of one. |
| Software Dependencies | No | The paper mentions 'GMMSeg is implemented on MMSegmentation [124]' but does not provide specific version numbers for MMSegmentation or any other software components (e.g., Python, PyTorch, CUDA) required for reproducibility. |
| Experiment Setup | Yes | For ADE20K/COCO-Stuff/Cityscapes, images are cropped to 512 512/512 512/768 768 and models are trained for 160K/80K/80K iterations with 16/16/8 batch size, using 8/16 NVIDIA Tesla A100 GPUs. |