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Token Contrast for Weakly-Supervised Semantic Segmentation

Code of CVPR 2023 paper: Token Contrast for Weakly-Supervised Semantic Segmentation.

[arXiv] [Poster]


AFA flowchart

We proposed Token Contrast to address the over-smoothing issue and further leverage the virtue of ViT for the Weakly-Supervised Semantic Segmentation task.

Data Preparations

VOC dataset

1. Download

wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
tar –xvf VOCtrainval_11-May-2012.tar

2. Download the augmented annotations

The augmented annotations are from SBD dataset. Here is a download link of the augmented annotations at DropBox. After downloading SegmentationClassAug.zip, you should unzip it and move it to VOCdevkit/VOC2012. The directory sctructure should thus be

VOCdevkit/
└── VOC2012
    ├── Annotations
    ├── ImageSets
    ├── JPEGImages
    ├── SegmentationClass
    ├── SegmentationClassAug
    └── SegmentationObject
COCO dataset

1. Download

wget http://images.cocodataset.org/zips/train2014.zip
wget http://images.cocodataset.org/zips/val2014.zip

2. Generating VOC style segmentation labels for COCO

To generate VOC style segmentation labels for COCO dataset, you could use the scripts provided at this repo. Or, just download the generated masks from Google Drive.

I recommend to organize the images and labels in coco2014 and SegmentationClass, respectively.

MSCOCO/
├── coco2014
│    ├── train2014
│    └── val2014
└── SegmentationClass
     ├── train2014
     └── val2014

Create environment

I used docker to build the enviroment.

## build docker
docker bulid -t toco --network=host -< Dockerfile

## activate docker
docker run -it --gpus all --network=host --ipc=host -v $CODE_PATH:/workspace/TOCO -v /$VOC_PATH:/workspace/VOCdevkit -v $COCO_ANNO_PATH:/workspace/MSCOCO -v $COCO_IMG_PATH:/workspace/coco2014 toco:latest /bin/bash

Clone this repo

git clone https://github.com/rulixiang/toco.git
cd toco

Build Reg Loss

To use the regularized loss, download and compile the python extension, see Here.

Train

To start training, just run:

## for VOC
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 --master_port=29501 scripts/dist_train_voc_seg_neg.py --work_dir work_dir_voc
## for COCO
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port=29501 scripts/dist_train_coco_seg_neg.py --work_dir work_dir_coco

Evalution

To evaluation:

## for VOC
python tools/infer_seg_voc.py --model_path $model_path --backbone vit_base_patch16_224 --infer val
## for COCO
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port=29501 tools/infer_seg_voc.py --model_path $model_path --backbone vit_base_patch16_224 --infer val

Results

Here we report the performance on VOC and COCO dataset. MS+CRF denotes multi-scale test and CRF processing.

Dataset Backbone val Log Weights val (with MS+CRF) test (with MS+CRF)
VOC DeiT-B 68.1 log weights 69.8 70.5
VOC ViT-B 69.2 log weights 71.1 72.2
COCO DeiT-B -- log weights 41.3 --
COCO ViT-B -- log weights 42.2 --

Citation

Please kindly cite our paper if you find it's helpful in your work.

@inproceedings{ru2023token,
    title = {Token Contrast for Weakly-Supervised Semantic Segmentation},
    author = {Lixiang Ru and Heliang Zheng and Yibing Zhan and Bo Du}
    booktitle = {CVPR},
    year = {2023},
  }

Acknowledgement

We mainly use ViT-B and DeiT-B as the backbone, which are based on timm. Also, we use the Regularized Loss. Many thanks to their brilliant works!