Papers › Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical...
Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision
Zhenzhen Weng, Mehmet Giray Ogut, Shai Limonchik, Serena Yeung
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Novel Object Detection | LVIS v1.0 val | Weng et al. Weng et al. (2021)* | All mAP | 1.62 | #5 of 5 | Archive leaderboard | report |
| Novel Object Detection | LVIS v1.0 val | Weng et al. Weng et al. (2021)* | Known mAP | 17.85 | #5 of 5 | Archive leaderboard | report |
| Novel Object Detection | LVIS v1.0 val | Weng et al. Weng et al. (2021)* | Novel mAP | 0.27 | #5 of 5 | Archive leaderboard | report |
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