StructToken

The official implementation of the paper " StructToken : Rethinking Semantic Segmentation with Structural Prior ".

Warning: This repository is still under construction!

Abstract

In previous deep-learning-based methods, semantic segmentation has been regarded as a static or dynamic per-pixel classification task, i.e., classify each pixel representation to a specific category. However, these methods only focus on learning better pixel representations or classification kernels while ignoring the structural information of objects, which is critical to human decision-making mechanism. In this paper, we present a new paradigm for semantic segmentation, named structure-aware extraction. Specifically, it generates the segmentation results via the interactions between a set of learnable structure tokens and the image feature, which aims to progressively extract the structural information of each category from the feature. Extensive experiments show that our StructToken outperforms the state-of-the-art on three widely-used benchmarks, including ADE20K, Cityscapes, and COCO-Stuff-10K.

Method

Catalog

• Initialization
• Code (in process)
• Checkpoints (in process)

Data Preparation

Please prepare ADE20K dataset according to the guidelines in MMSegmentation.

Pre-training Sources

Please prepare pretrain checkpoints and put them in repo_directory/pretrain folder according to the guideline in MMSegmentation

Download Checkpoints

You can download our checkpoints using the the download script tools/download_ckpts.py. It has the following parameters:

• --keys: (optional) The keys of the checkpoints that you want to download.
• --folder: (optional) The directory of the folder that you want to download the checkpoints to. Default to ./checkpoints.
• --ckpt-names: (optional) The save names of the checkpoints you want to download. Default to {key}.pth.
• --all: (optional) Download all the checkpoints.

For example, if you want to download the checkpoints with keys struct-token-cse_vit-b_ade20k and struct-token-sse_vit-b_ade20k, and then save them with save names cse_vit-b_ade20k.pth and sse_vit-b_ade20k.pth, you can use:

python tools/download_ckpts.py --keys struct-token-cse_vit-b_ade20k struct-token-sse_vit-b_ade20k --ckpt-names cse_vit-b_ade20k.pth sse_vit-b_ade20k.pth

Then these two checkpoints will be downloaded to ./checkpoints folder with names cse_vit-b_ade20k.pth and sse_vit-b_ade20k.pth.

Another example, if you want to download all checkpoints, then you can use:

python tools/download_ckpts.py --all

Then all checkpoints will be downloaded to ./checkpoints folder.

Results and Models

ADE20K Val

Method	Backbone	Lr Schedule	Crop Size	mIoU(ss)	mIoU(ms)	Config	Download Key
StructToken-CSE	ViT-T	160k	512x512	39.12	40.23	config	struct-token-cse_vit-t_ade20k
StructToken-PWE	ViT-T	160k	512x512	41.87	42.99	config	struct-token-pwe_vit-t_ade20k
StructToken-SSE	ViT-T	160k	512x512	40.81	42.24	config	struct-token-sse_vit-t_ade20k
StructToken-CSE	ViT-S	160k	512x512	45.86	47.44	config	struct-token-cse_vit-s_ade20k
StructToken-PWE	ViT-S	160k	512x512	47.36	48.89	config	struct-token-pwe_vit-s_ade20k
StructToken-SSE	ViT-S	160k	512x512	47.11	49.07	config	struct-token-sse_vit-s_ade20k
StructToken-CSE	ViT-B	160k	512x512	49.51	50.87	config	struct-token-cse_vit-b_ade20k
StructToken-PWE	ViT-B	160k	512x512	50.92	51.82	config	struct-token-pwe_vit-b_ade20k
StructToken-SSE	ViT-B	160k	512x512	50.72	51.85	config	struct-token-sse_vit-b_ade20k
StructToken-PWE	ViT-L	160k	640x640	52.95	54.03	config	struct-token-pwe_vit-l_ade20k
StructToken-SSE	ViT-L	160k	640x640	53.04	53.95	config	struct-token-sse_vit-l_ade20k

Evaluation

To evaluate a model whose config directory is path/to/config and checkpoint directory is path/to/checkpoint on a single node with 8 gpus, please run:

sh tools/dist_test.sh path/to/config path/to/checkpoint 8 --eval mIoU

Training

To train a model whose config directory is path/to/config on a single node with 8 gpus, please run:

sh tools/dist_train.sh path/to/config 8

Citation

If this work is helpful for your research, please consider citing the following BibTeX entry.

@article{lin2022structtoken,
  title={StructToken: Rethinking Semantic Segmentation with Structural Prior},
  author={Lin, Fangjian and Liang, Zhanhao and He, Junjun and Zheng, Miao and Tian, Shengwei and Chen, Kai},
  journal={arXiv preprint arXiv:2203.12612},
  year={2022}
}

License

This repository is released under the Apache 2.0 license as found in the LICENSE file.