Readme
Cut and Learn for Unsupervised Object Detection and Instance Segmentation
Cut -and- LE a R n ( CutLER ) is a simple approach for training object detection and instance segmentation models without human annotations. It outperforms previous SOTA by 2.7 times for AP50 and 2.6 times for AR on 11 benchmarks .
Features
- We propose MaskCut approach to generate pseudo-masks for multiple objects in an image.
- CutLER can learn unsupervised object detectors and instance segmentors solely on ImageNet-1K.
- CutLER exhibits strong robustness to domain shifts when evaluated on 11 different benchmarks across domains like natural images, video frames, paintings, sketches, etc.
- CutLER can serve as a pretrained model for fully/semi-supervised detection and segmentation tasks.
Citation
@article{wang2023cut,
title={Cut and Learn for Unsupervised Object Detection and Instance Segmentation},
author={Wang, Xudong and Girdhar, Rohit and Yu, Stella X and Misra, Ishan},
journal={arXiv preprint arXiv:2301.11320},
year={2023}
}