Abstract

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%.

Keywords

Computer scienceArtificial intelligenceSegmentationEmbeddingPascal (unit)Discriminative modelPattern recognition (psychology)ExploitScale-space segmentationImage segmentationComputer vision

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Publication Info

Year
2019
Type
article
Pages
9196-9205
Citations
1244
Access
Closed

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Cite This

Kaixin Wang, Jun Hao Liew, Yingtian Zou et al. (2019). PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment. , 9196-9205. https://doi.org/10.1109/iccv.2019.00929

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DOI
10.1109/iccv.2019.00929