Detect and segment unknown objects with unsupervised learning using graph partitioning with CutLER
Detect and segment unknown objects with unsupervised learning using graph partitioning with CutLER
Cut and Learn for Unsupervised Object Detection and Instance Segmentation
arXiv paper abstract https://arxiv.org/abs/2301.11320
arXiv PDF paper https://arxiv.org/pdf/2301.11320.pdf
... propose Cut-and-LEaRn (CutLER), a simple approach for training unsupervised object detection and segmentation models.
... 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 ... proposed MaskCut approach to generate coarse masks for multiple objects in an image and then learns a detector on these masks
... 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 ... 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 ...
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