Few-Shot Incremental Learning With Context-Aware Spatial Enhancement for Image Recognition
Few-shot incremental learning (FSIL) refers to the ability of a model to learn new concepts from a limited number of labeled examples and gradually recognize novel categories with minimal supervision while retaining previously learned knowledge to prevent forgetting. To address the key challenges in...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11036738/ |
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| author | Heng Wu Ze Yang Zijun Zheng Haiyang Wang Wansong Wang |
| author_facet | Heng Wu Ze Yang Zijun Zheng Haiyang Wang Wansong Wang |
| author_sort | Heng Wu |
| collection | DOAJ |
| description | Few-shot incremental learning (FSIL) refers to the ability of a model to learn new concepts from a limited number of labeled examples and gradually recognize novel categories with minimal supervision while retaining previously learned knowledge to prevent forgetting. To address the key challenges in FSIL, this paper proposes a novel Context-Aware Spatial Enhancement (CASE) framework, which improves feature representations by jointly leveraging global and local spatial information. Specifically, the global spatial enhancement module captures long-range dependencies to enrich semantic context, while the local spatial enhancement module applies Gaussian filtering to refine fine-grained details and suppress background noise. Additionally, a multi-head interaction block is introduced to model intricate relationships between spatial regions, effectively bridging global and local perspectives for robust and context-aware feature learning. CASE also incorporates prior knowledge from base categories to enhance adaptability to novel classes. This comprehensive design not only reduces background interference and highlights salient features but also improves generalization and stability in few-shot learning scenarios. Extensive experiments on benchmark datasets validate the effectiveness of the proposed approach, demonstrating competitive performance along with superior scalability and generalization capability in FSIL tasks. |
| format | Article |
| id | doaj-art-a6b6ef122e9f4e24a9ebcdb0c1e87576 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a6b6ef122e9f4e24a9ebcdb0c1e875762025-08-20T03:29:34ZengIEEEIEEE Access2169-35362025-01-011311056911058310.1109/ACCESS.2025.357976611036738Few-Shot Incremental Learning With Context-Aware Spatial Enhancement for Image RecognitionHeng Wu0https://orcid.org/0000-0002-4632-0339Ze Yang1Zijun Zheng2Haiyang Wang3Wansong Wang4https://orcid.org/0000-0003-2855-8228College of Information Engineering, Hangzhou Vocational and Technical College, Hangzhou, ChinaChina Mobile Information System Integration Company Ltd., Beijing, ChinaCollege of Sciences, China Jiliang University, Hangzhou, ChinaInstitute of Network Technology (Yantai), Yantai, ChinaDepartment of Rehabilitation Medicine, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, ChinaFew-shot incremental learning (FSIL) refers to the ability of a model to learn new concepts from a limited number of labeled examples and gradually recognize novel categories with minimal supervision while retaining previously learned knowledge to prevent forgetting. To address the key challenges in FSIL, this paper proposes a novel Context-Aware Spatial Enhancement (CASE) framework, which improves feature representations by jointly leveraging global and local spatial information. Specifically, the global spatial enhancement module captures long-range dependencies to enrich semantic context, while the local spatial enhancement module applies Gaussian filtering to refine fine-grained details and suppress background noise. Additionally, a multi-head interaction block is introduced to model intricate relationships between spatial regions, effectively bridging global and local perspectives for robust and context-aware feature learning. CASE also incorporates prior knowledge from base categories to enhance adaptability to novel classes. This comprehensive design not only reduces background interference and highlights salient features but also improves generalization and stability in few-shot learning scenarios. Extensive experiments on benchmark datasets validate the effectiveness of the proposed approach, demonstrating competitive performance along with superior scalability and generalization capability in FSIL tasks.https://ieeexplore.ieee.org/document/11036738/Few-shot incremental learningimage recognitioncontext-aware spatial enhancement |
| spellingShingle | Heng Wu Ze Yang Zijun Zheng Haiyang Wang Wansong Wang Few-Shot Incremental Learning With Context-Aware Spatial Enhancement for Image Recognition IEEE Access Few-shot incremental learning image recognition context-aware spatial enhancement |
| title | Few-Shot Incremental Learning With Context-Aware Spatial Enhancement for Image Recognition |
| title_full | Few-Shot Incremental Learning With Context-Aware Spatial Enhancement for Image Recognition |
| title_fullStr | Few-Shot Incremental Learning With Context-Aware Spatial Enhancement for Image Recognition |
| title_full_unstemmed | Few-Shot Incremental Learning With Context-Aware Spatial Enhancement for Image Recognition |
| title_short | Few-Shot Incremental Learning With Context-Aware Spatial Enhancement for Image Recognition |
| title_sort | few shot incremental learning with context aware spatial enhancement for image recognition |
| topic | Few-shot incremental learning image recognition context-aware spatial enhancement |
| url | https://ieeexplore.ieee.org/document/11036738/ |
| work_keys_str_mv | AT hengwu fewshotincrementallearningwithcontextawarespatialenhancementforimagerecognition AT zeyang fewshotincrementallearningwithcontextawarespatialenhancementforimagerecognition AT zijunzheng fewshotincrementallearningwithcontextawarespatialenhancementforimagerecognition AT haiyangwang fewshotincrementallearningwithcontextawarespatialenhancementforimagerecognition AT wansongwang fewshotincrementallearningwithcontextawarespatialenhancementforimagerecognition |