Few-Shot Segmentation Using Multi-Similarity and Attention Guidance
Few-shot segmentation (FSS) methods aim to segment objects of novel classes with relatively few annotated samples. Prototype learning, a popular approach in FSS, employs prototype vectors to transfer information from known classes (support images) to novel classes(query images) for segmentation. How...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Computer Society |
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| Online Access: | https://ieeexplore.ieee.org/document/11095423/ |
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| author | Ehtesham Iqbal Sirojbek Safarov Seongdeok Bang Sajid Javed Yahya Zweiri Yusra Abdulrahman |
| author_facet | Ehtesham Iqbal Sirojbek Safarov Seongdeok Bang Sajid Javed Yahya Zweiri Yusra Abdulrahman |
| author_sort | Ehtesham Iqbal |
| collection | DOAJ |
| description | Few-shot segmentation (FSS) methods aim to segment objects of novel classes with relatively few annotated samples. Prototype learning, a popular approach in FSS, employs prototype vectors to transfer information from known classes (support images) to novel classes(query images) for segmentation. However, using only prototype vectors may not be sufficient to represent all features of the support image. To extract abundant features and make more precise predictions, we propose a <bold>M</bold>ulti-<bold>S</bold>imilarity and <bold>A</bold>ttention <bold>N</bold>etwork (MSANet) including two novel modules, a multi-similarity module and an attention module. The multi-similarity module exploits multiple feature-map of support images and query images to estimate accurate semantic relationships. The attention module instructs the MSANet to concentrate on class-relevant information. We evaluated the proposed network on standard FSS datasets, PASCAL-<inline-formula><tex-math notation="LaTeX">$5^{i}$</tex-math></inline-formula> 1-shot, PASCAL-<inline-formula><tex-math notation="LaTeX">$5^{i}$</tex-math></inline-formula> 5-shot, COCO-<inline-formula><tex-math notation="LaTeX">$20^{i}$</tex-math></inline-formula> 1-shot, and COCO-<inline-formula><tex-math notation="LaTeX">$20^{i}$</tex-math></inline-formula> 5-shot. An MSANet model with a ResNet101 backbone achieved state-of-the-art performance for all four benchmark datasets with mean intersection over union (mIoU) values of 69.13%, 73.99%, 51.09%, and 56.80%, respectively. The code used is available at <uri>https://github.com/AIVResearch/MSANet</uri>. |
| format | Article |
| id | doaj-art-fef0e45fa1a2489ebb5de2d3a90091a1 |
| institution | DOAJ |
| issn | 2644-1268 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Computer Society |
| spelling | doaj-art-fef0e45fa1a2489ebb5de2d3a90091a12025-08-20T03:05:50ZengIEEEIEEE Open Journal of the Computer Society2644-12682025-01-0161271128210.1109/OJCS.2025.359229111095423Few-Shot Segmentation Using Multi-Similarity and Attention GuidanceEhtesham Iqbal0https://orcid.org/0000-0003-1798-036XSirojbek Safarov1https://orcid.org/0000-0001-5724-3271Seongdeok Bang2https://orcid.org/0009-0003-1745-3217Sajid Javed3https://orcid.org/0000-0002-0036-2875Yahya Zweiri4https://orcid.org/0000-0003-4331-7254Yusra Abdulrahman5https://orcid.org/0000-0003-1211-8498Advanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi, UAEAiV Research Group, Yusung-ku, South KoreaAiV Research Group, Yusung-ku, South KoreaDepartment of Computer Science, Khalifa University of Science and Technology, Abu Dhabi, UAEAdvanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi, UAEAdvanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi, UAEFew-shot segmentation (FSS) methods aim to segment objects of novel classes with relatively few annotated samples. Prototype learning, a popular approach in FSS, employs prototype vectors to transfer information from known classes (support images) to novel classes(query images) for segmentation. However, using only prototype vectors may not be sufficient to represent all features of the support image. To extract abundant features and make more precise predictions, we propose a <bold>M</bold>ulti-<bold>S</bold>imilarity and <bold>A</bold>ttention <bold>N</bold>etwork (MSANet) including two novel modules, a multi-similarity module and an attention module. The multi-similarity module exploits multiple feature-map of support images and query images to estimate accurate semantic relationships. The attention module instructs the MSANet to concentrate on class-relevant information. We evaluated the proposed network on standard FSS datasets, PASCAL-<inline-formula><tex-math notation="LaTeX">$5^{i}$</tex-math></inline-formula> 1-shot, PASCAL-<inline-formula><tex-math notation="LaTeX">$5^{i}$</tex-math></inline-formula> 5-shot, COCO-<inline-formula><tex-math notation="LaTeX">$20^{i}$</tex-math></inline-formula> 1-shot, and COCO-<inline-formula><tex-math notation="LaTeX">$20^{i}$</tex-math></inline-formula> 5-shot. An MSANet model with a ResNet101 backbone achieved state-of-the-art performance for all four benchmark datasets with mean intersection over union (mIoU) values of 69.13%, 73.99%, 51.09%, and 56.80%, respectively. The code used is available at <uri>https://github.com/AIVResearch/MSANet</uri>.https://ieeexplore.ieee.org/document/11095423/Few-shot learningImage segmentationDeep learning |
| spellingShingle | Ehtesham Iqbal Sirojbek Safarov Seongdeok Bang Sajid Javed Yahya Zweiri Yusra Abdulrahman Few-Shot Segmentation Using Multi-Similarity and Attention Guidance IEEE Open Journal of the Computer Society Few-shot learning Image segmentation Deep learning |
| title | Few-Shot Segmentation Using Multi-Similarity and Attention Guidance |
| title_full | Few-Shot Segmentation Using Multi-Similarity and Attention Guidance |
| title_fullStr | Few-Shot Segmentation Using Multi-Similarity and Attention Guidance |
| title_full_unstemmed | Few-Shot Segmentation Using Multi-Similarity and Attention Guidance |
| title_short | Few-Shot Segmentation Using Multi-Similarity and Attention Guidance |
| title_sort | few shot segmentation using multi similarity and attention guidance |
| topic | Few-shot learning Image segmentation Deep learning |
| url | https://ieeexplore.ieee.org/document/11095423/ |
| work_keys_str_mv | AT ehteshamiqbal fewshotsegmentationusingmultisimilarityandattentionguidance AT sirojbeksafarov fewshotsegmentationusingmultisimilarityandattentionguidance AT seongdeokbang fewshotsegmentationusingmultisimilarityandattentionguidance AT sajidjaved fewshotsegmentationusingmultisimilarityandattentionguidance AT yahyazweiri fewshotsegmentationusingmultisimilarityandattentionguidance AT yusraabdulrahman fewshotsegmentationusingmultisimilarityandattentionguidance |