Detect and segment unknown objects with unsupervised learning using graph partitioning with CutLER
- morrislee
- Jan 27, 2023
- 1 min read
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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