Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision

• URL: http://arxiv.org/abs/2104.01257v1
• Date: Fri, 2 Apr 2021 22:05:03 GMT
• Title: Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision
• Authors: Zhenzhen Weng, Mehmet Giray Ogut, Shai Limonchik, Serena Yeung
• Abstract summary: We propose a method that can perform unsupervised discovery of long-tail categories in instance segmentation. Our model is able to discover novel and more fine-grained objects than the common categories. We show that the model achieves competitive quantitative results on LVIS as compared to the supervised and partially supervised methods.
• Score: 3.841232411073827
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Instance segmentation is an active topic in computer vision that is usually solved by using supervised learning approaches over very large datasets composed of object level masks. Obtaining such a dataset for any new domain can be very expensive and time-consuming. In addition, models trained on certain annotated categories do not generalize well to unseen objects. The goal of this paper is to propose a method that can perform unsupervised discovery of long-tail categories in instance segmentation, through learning instance embeddings of masked regions. Leveraging rich relationship and hierarchical structure between objects in the images, we propose self-supervised losses for learning mask embeddings. Trained on COCO dataset without additional annotations of the long-tail objects, our model is able to discover novel and more fine-grained objects than the common categories in COCO. We show that the model achieves competitive quantitative results on LVIS as compared to the supervised and partially supervised methods.

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