Method evidence record
Semi-supervised Instance Segmentation
Semi-supervised instance segmentation trains a model to detect and delineate every object instance in an image using a small labeled set and a large unlabeled image corpus. By generating pseudo-labels from confident predictions on unlabeled images and enforcing consistency under augmentation, the approach achieves competitive mask accuracy at a fraction of the full annotation cost.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
Semi-supervised Instance Segmentation
Taxonomic method record · ml-model / deep-learning
- Hu, H., Wei, P., Zheng, H., Bai, X., Wei, Y., & Chen, Y. (2021). Semi-supervised Semantic Segmentation via Adaptive Equalization Learning. Advances in Neural Information Processing Systems (NeurIPS), 34, 22106–22118. · URL
- Xu, M., Zhang, Z., Wei, F., Hu, H., Bai, X., & Jiang, Y.-G. (2021). End-to-End Semi-Supervised Object Detection with Soft Teacher. IEEE/CVF International Conference on Computer Vision (ICCV), 3060–3069. · URL
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
No curated claims yet
This view does not invent a claim assessment when the ledger has none.
Related methods
Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.