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Cut and Learn for Unsupervised Object Detection and Instance Segmentation
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Xudong Wang and Rohit Girdhar and Stella X. Yu and Ishan Misra
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IEEE Conference on Computer Vision and Pattern Recognition, Vancouver, British Columbia, Canada, 2023
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Paper
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Slides
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Code
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arXiv
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Abstract
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We propose Cut-and-LEaRn (CutLER), a simple approach for training unsupervised object detection and segmentation models. We leverage the property of self-supervised models to 'discover' objects without supervision and amplify it to train a state-of-the-art localization model without any human labels. CutLER first uses our proposed MaskCut approach to generate coarse masks for multiple objects in an image and then learns a detector on these masks using our robust loss function. We further improve the performance by self-training the model on its predictions. Compared to prior work, CutLER is simpler, compatible with different detection architectures, and detects multiple objects. CutLER is also a zero-shot unsupervised detector and improves detection performance AP50 by over 2.7 times on 11 benchmarks across domains like video frames, paintings, sketches, etc. With finetuning, CutLER serves as a low-shot detector surpassing MoCo-v2 by 7.3\% APbox and 6.6\% APmask on COCO when training with 5\% labels.
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Keywords
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unsupervised object detection, unsupervised instance segmentation, normalized cuts
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