Publisher
Springer Science and Business Media LLC
Reference70 articles.
1. Angus, M., Czarnecki, K., & Salay, R. (2019). Efficacy of pixel-level ood detection for semantic segmentation. arXiv preprint arXiv:1911.02897
2. Brown, T., Mann, B., Ryder, N., et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
3. Bucher, M., Vu, T. H., Cord, M., et al. (2019). Zero-shot semantic segmentation. Advances in Neural Information Processing Systems. https://doi.org/10.48550/arXiv.1906.00817
4. Caesar, H., Uijlings, JRR. & Ferrari, V. (2018). Coco-stuff: Thing and stuff classes in context. In: Proceedings of the IEEE conference on computer vision and pattern recognition, computer vision foundation / IEEE computer society, pp 1209–1218
5. Cen, J., Yun, P., Cai, J., et al. (2021). Deep metric learning for open world semantic segmentation. In International conference on computer vision , pp 15,333–15,342