Self-Support Few-Shot Semantic Segmentation

Qi Fan, Wenjie Pei, Yu-Wing Tai, Chi-Keung Tang

The codebase contains the official code of our paper Self-Support Few-Shot Semantic Segmentation, ECCV 2022.

Data preparation

Download

Pretrained model: ResNet-50 | ResNet-101

Dataset: Pascal images and ids | Semantic segmentation annotations

File Organization

├── ./pretrained
    ├── resnet50.pth
    └── resnet101.pth
    
├── [Your Pascal Path]
    ├── JPEGImages
    │   ├── 2007_000032.jpg
    │   └── ...
    │
    ├── SegmentationClass
    │   ├── 2007_000032.png
    │   └── ...
    │
    └── ImageSets
        ├── train.txt
        └── val.txt

Run the code

CUDA_VISIBLE_DEVICES=0,1 python -W ignore main.py \
  --dataset pascal --data-root [Your Pascal Path] \
  --backbone resnet50 --fold 0 --shot 1

You may change the backbone from resnet50 to resnet101, change the fold from 0 to 1/2/3, or change the shot from 1 to 5 for other settings.

Performance and Trained Models

Pascal VOC

Method	Setting	Backbone	Refinement	Fold 0	Fold 1	Fold 2	Fold 3	Mean
Baseline	1-shot	ResNet-50	No	54.9	66.5	61.7	48.3	57.9
Baseline	1-shot	ResNet-101	No	57.2	68.5	61.3	53.3	60.1
Baseline	5-shot	ResNet-50	No	61.6	70.3	70.5	56.4	64.7
Baseline	5-shot	ResNet-101	No	64.2	74.0	71.5	61.3	67.8
SSP (Ours)	1-shot	ResNet-50	Yes	61.4	67.8	66.5	50.9	61.7
SSP (Ours)	1-shot	ResNet-101	Yes	63.2	70.4	68.5	56.3	64.6
SSP (Ours)	5-shot	ResNet-50	Yes	67.5	72.3	75.2	62.1	69.3
SSP (Ours)	5-shot	ResNet-101	Yes	70.9	77.1	78.9	66.1	73.3

MS COCO

Acknowledgement

This codebase is built based on MLC's baseline code. We thank MLC and other FSS works for their great contributions.

Citation

@inproceedings{fan2022ssp,
  title={Self-Support Few-Shot Semantic Segmentation},
  author={Fan, Qi and Pei, Wenjie and Tai, Yu-Wing and Tang, Chi-Keung},
  journal={ECCV},
  year={2022}
}