Few-Shot Semantic Segmentation Network for Distinguishing Positive and Negative Examples
Few-shot segmentation (FSS) aims to segment a query image with a few support images. However, there can be large differences between images from the same category, and similarities between different categories, making it a challenging task. In addition, most FSS methods use powerful encoders to extr...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/7/3627 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850184640774537216 |
|---|---|
| author | Feng Guo Dong Zhou |
| author_facet | Feng Guo Dong Zhou |
| author_sort | Feng Guo |
| collection | DOAJ |
| description | Few-shot segmentation (FSS) aims to segment a query image with a few support images. However, there can be large differences between images from the same category, and similarities between different categories, making it a challenging task. In addition, most FSS methods use powerful encoders to extract features from the training class, which makes the model pay more attention to the features of the ‘seen’ class, and perform poorly on the segmentation task of ‘unseen’ classes. In this work, we propose a novel end-to-end model, called GFormer. GFormer has four components: encoder, prototype extractor, adversarial prototype generator, and decoder. Our encoder makes simple modifications to VIT to reduce the focus on image content, using a prototype extractor to extract prototype features from a range of support images. We further introduce different classes that are similar to the support image categories as negative examples, taking the support image categories as positive examples. We use the adversarial prototype generator to extract the adversarial prototypes from the positive and negative examples. The decoder segments the query images under the guidance of the prototypes. We conduct extensive experiments on a variety of unknown classes. The results verify the feasibility of the proposed model and prove that the proposed model has strong generalization performance for new classes. |
| format | Article |
| id | doaj-art-2e6202f771b04a818e2f803c77656cf9 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-2e6202f771b04a818e2f803c77656cf92025-08-20T02:17:00ZengMDPI AGApplied Sciences2076-34172025-03-01157362710.3390/app15073627Few-Shot Semantic Segmentation Network for Distinguishing Positive and Negative ExamplesFeng Guo0Dong Zhou1Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, ChinaResearch Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, ChinaFew-shot segmentation (FSS) aims to segment a query image with a few support images. However, there can be large differences between images from the same category, and similarities between different categories, making it a challenging task. In addition, most FSS methods use powerful encoders to extract features from the training class, which makes the model pay more attention to the features of the ‘seen’ class, and perform poorly on the segmentation task of ‘unseen’ classes. In this work, we propose a novel end-to-end model, called GFormer. GFormer has four components: encoder, prototype extractor, adversarial prototype generator, and decoder. Our encoder makes simple modifications to VIT to reduce the focus on image content, using a prototype extractor to extract prototype features from a range of support images. We further introduce different classes that are similar to the support image categories as negative examples, taking the support image categories as positive examples. We use the adversarial prototype generator to extract the adversarial prototypes from the positive and negative examples. The decoder segments the query images under the guidance of the prototypes. We conduct extensive experiments on a variety of unknown classes. The results verify the feasibility of the proposed model and prove that the proposed model has strong generalization performance for new classes.https://www.mdpi.com/2076-3417/15/7/3627few-shot semantic segmentationprototype learningtransformer |
| spellingShingle | Feng Guo Dong Zhou Few-Shot Semantic Segmentation Network for Distinguishing Positive and Negative Examples Applied Sciences few-shot semantic segmentation prototype learning transformer |
| title | Few-Shot Semantic Segmentation Network for Distinguishing Positive and Negative Examples |
| title_full | Few-Shot Semantic Segmentation Network for Distinguishing Positive and Negative Examples |
| title_fullStr | Few-Shot Semantic Segmentation Network for Distinguishing Positive and Negative Examples |
| title_full_unstemmed | Few-Shot Semantic Segmentation Network for Distinguishing Positive and Negative Examples |
| title_short | Few-Shot Semantic Segmentation Network for Distinguishing Positive and Negative Examples |
| title_sort | few shot semantic segmentation network for distinguishing positive and negative examples |
| topic | few-shot semantic segmentation prototype learning transformer |
| url | https://www.mdpi.com/2076-3417/15/7/3627 |
| work_keys_str_mv | AT fengguo fewshotsemanticsegmentationnetworkfordistinguishingpositiveandnegativeexamples AT dongzhou fewshotsemanticsegmentationnetworkfordistinguishingpositiveandnegativeexamples |