GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models
Authors: Chen Liang, Wenguan Wang, Jiaxu Miao, Yi Yang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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. |