​​Cut and Learn for Unsupervised Object Detection and Instance | Data Science by ODS.ai 🦜

​​Cut and Learn for Unsupervised Object Detection and Instance Segmentation

CutLER (Cut-and-LEaRn) is a new approach for training unsupervised object detection and segmentation models without using any human labels. It uses a combination of a MaskCut approach to generate object masks and a robust loss function to learn a detector. The model is simple and compatible with different detection architectures and can detect multiple objects. It is a zero-shot detector, meaning it performs well without additional in-domain data and is robust against domain shifts across various types of images. CutLER can also be used as a pretrained model for supervised detection and improves performance on few-shot benchmarks. Results show improved performance over previous work, including being a zero-shot unsupervised detector and surpassing other low-shot detectors with finetuning.

Paper: https://arxiv.org/abs/2301.11320

Code link: https://github.com/facebookresearch/CutLER1

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-cutler

#deeplearning #cv #objectdetection #imagesegmentation