The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment
Open accessAbstract
Despite the great progress made by deep CNNs in image semantic segmentation, they typically require a large number of densely-annotated images for training and are difficult to generalize to unseen object categories. Few-shot segmentation has thus been developed to learn to perform segmentation from only a few annotated examples. In this paper, we tackle the challenging few-shot segmentation problem from a metric learning perspective and present PANet, a novel prototype alignment network to better utilize the information of the support set. Our PANet learns class-specific prototype representations from a few support images within an embedding space and then performs segmentation over the query images through matching each pixel to the learned prototypes. With non-parametric metric learning, PANet offers high-quality prototypes that are representative for each semantic class and meanwhile discriminative for different classes. Moreover, PANet introduces a prototype alignment regularization between support and query. With this, PANet fully exploits knowledge from the support and provides better generalization on few-shot segmentation. Significantly, our model achieves the mIoU score of 48.1% and 55.7% on PASCAL-5i for 1-shot and 5-shot settings respectively, surpassing the state-of-the-art method by 1.8% and 8.6%.
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Cited by
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities
Song, Y., Wang, T., Cai, P., Mondal, S. K., & Sahoo, J. P. (2023) · ACM Computing Surveys · doi:10.1145/3582688 · Cited by 721
A Forest Fire Detection System Based on Ensemble Learning
Xu, R., Lin, H., Lu, K., Cao, L., & Liu, Y. (2021) · Forests · doi:10.3390/f12020217 · Cited by 559
Wu, D., Lv, S., Jiang, M., & Song, H. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105742 · Cited by 496
Prototype Mixture Models for Few-Shot Semantic Segmentation
Yang, B., Liu, C., Li, B., Jiao, J., & Ye, Q. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58598-3_45 · Cited by 412
Liang, S., Wu, H., Zhen, L., Hua, Q., Garg, S., Kaddoum, G., Hassan, M. M., & Yu, K. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3158253 · Cited by 367
Metadata
- Authors
- Wang, K., Liew, J. H., Zou, Y., Zhou, D., & Feng, J.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 9196-9205
- OpenAlex
- W2990230185
- Cited by
- 1,500
- References
- 26
- FWCI
- 38.76
- Type
- conference paper
- Language
- en
- Open access
- green
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- Wang, K., Liew, J. H., Zou, Y., Zhou, D., & Feng, J. (2019). PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2019.00929
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