Awesome

Saliency Prompt

This code contains our extended version based on CVPR.

Timeline

:triangular_flag_on_post: Updates

• Releasing Unsupervised Pre-training with Language-Vision Prompts for Low-Data Instance Segmentation.
• Our Boosting low-data instance segmentation by unsupervised pre-training with saliency prompt has been accepted by CVPR 2023.

@article{zhang2024unsupervised,
  title={Unsupervised Pre-training with Language-Vision Prompts for Low-Data Instance Segmentation},
  author={Zhang, Dingwen and Li, Hao and He, Diqi and Liu, Nian and Cheng, Lechao and Wang, Jingdong and Han, Junwei},
  journal={arXiv preprint arXiv:2405.13388},
  year={2024}
}
@inproceedings{li2023boosting,
  title={Boosting low-data instance segmentation by unsupervised pre-training with saliency prompt},
  author={Li, Hao and Zhang, Dingwen and Liu, Nian and Cheng, Lechao and Dai, Yalun and Zhang, Chao and Wang, Xinggang and Han, Junwei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={15485--15494},
  year={2023}
}

Requirements

• Python 3.8
• CUDA 11.3
• PyTorch 1.10.0
• mmdet 2.19.0
• mmcv-full 1.4.8
• mmselfsup 0.10.0

File Structure

UPLVP/
├── configs/
├── custommd/
├── tools_det/
README.md

Usage

Mask Proposal

Requirements:

• clip 1.0
• tensorflow-gpu 2.6.0
• pycocotools 2.0.0

To generate pseudo masks run:

python tools_det/maskproposal.py

Pre-train

To pre-train K-Net/Mask2former/QueryInst with 8 gpus run:

bash tools_det/dist_train.sh configs/selfsup/upknet/upknet_feature_coco_pretrain_labeled_prompt_ann.py 8

or

bash tools_det/dist_train.sh configs/selfsup/upmask2former/upmask2former_r50_lsj_8x2_50e_coco_prompt_ann.py 8

or

bash tools_det/dist_train.sh configs/selfsup/upqueryinst/upqueryinst_r50_fpn_1x_coco_pretrain_moco_labeled_prompt.py 8

Fine-tune

To fine-tune K-Net with 8 gpus on COCO-10%/Cityscapes/CTW1500 run:

bash tools_det/dist_train.sh configs/det/knet/upknet_inherit_coco_10image_openseg_ann.py 8

or

bash tools_det/dist_train.sh configs/det/knet/upknet_s3_r50_fpn_1x_cityscapes.py 8

or

bash tools_det/dist_train.sh configs/det/knet/upknet_s3_r50_fpn_1x_ctw1500.py 8

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant 62293543, Grant U21B2048 and Grant 62106235.