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Papers/Understanding Self-Supervised Features for Learning Unsupe...

Understanding Self-Supervised Features for Learning Unsupervised Instance Segmentation

Paul Engstler, Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina

2023-11-24Self-Supervised LearningUnsupervised Semantic SegmentationSegmentationSemantic SegmentationInstance SegmentationUnsupervised Instance Segmentation
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Abstract

Self-supervised learning (SSL) can be used to solve complex visual tasks without human labels. Self-supervised representations encode useful semantic information about images, and as a result, they have already been used for tasks such as unsupervised semantic segmentation. In this paper, we investigate self-supervised representations for instance segmentation without any manual annotations. We find that the features of different SSL methods vary in their level of instance-awareness. In particular, DINO features, which are known to be excellent semantic descriptors, lack behind MAE features in their sensitivity for separating instances.

Results

TaskDatasetMetricValueModel
Unsupervised Instance SegmentationCOCO val2017AP5.2Self-Training (MAE)
Unsupervised Instance SegmentationCOCO val2017AP5012.1Self-Training (MAE)
Unsupervised Instance SegmentationCOCO val2017AP753.7Self-Training (MAE)

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