The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation
Open accessAbstract
One-shot image semantic segmentation poses a challenging task of recognizing the object regions from unseen categories with only one annotated example as supervision. In this article, we propose a simple yet effective similarity guidance network to tackle the one-shot (SG-One) segmentation problem. We aim at predicting the segmentation mask of a query image with the reference to one densely labeled support image of the same category. To obtain the robust representative feature of the support image, we first adopt a masked average pooling strategy for producing the guidance features by only taking the pixels belonging to the support image into account. We then leverage the cosine similarity to build the relationship between the guidance features and features of pixels from the query image. In this way, the possibilities embedded in the produced similarity maps can be adopted to guide the process of segmenting objects. Furthermore, our SG-One is a unified framework that can efficiently process both support and query images within one network and be learned in an end-to-end manner. We conduct extensive experiments on Pascal VOC 2012. In particular, our SG-One achieves the mIoU score of 46.3%, surpassing the baseline methods.
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U-Net: Convolutional Networks for Biomedical Image Segmentation
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ImageNet Large Scale Visual Recognition Challenge
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Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
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The Pascal Visual Object Classes Challenge: A Retrospective
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PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment
Wang, K., Liew, J. H., Zou, Y., Zhou, D., & Feng, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00929 · Cited by 1,500
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities
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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
Pyramid Graph Networks With Connection Attentions for Region-Based One-Shot Semantic Segmentation
Zhang, C., Lin, G., Liu, F., Guo, J., Wu, Q., & Yao, R. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00968 · Cited by 346
Triplet-Graph Reasoning Network for Few-Shot Metal Generic Surface Defect Segmentation
Bao, Y., Song, K., Liu, J., Wang, Y., Yan, Y., Yu, H., & Li, X. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3083561 · Cited by 319
Metadata
- Authors
- Zhang, X., Wei, Y., Yang, Y., & Huang, T. S.
- Venue
- IEEE Transactions on Cybernetics
- Published
- 2020-09
- Volume
- 50
- Issue
- 9
- Pages
- 3855-3865
- ISSN
- 2168-2267 (ISSN-L)
- 2168-2275
- OpenAlex
- W3033502887
- Cited by
- 570
- References
- 11
- FWCI
- 28.70
- Type
- journal article
- Language
- en
- Open access
- green
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