Efficient Semantic Segmentation for Resource-Constrained Applications with Lightweight Neural Networks
This thesis focuses on developing lightweight semantic segmentation models tailored for resource-constrained applications, effectively balancing accuracy and computational efficiency. It introduces several novel concepts, including knowledge sharing, dense bottleneck, and feature re-usability, which...
| Main Author: | |
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| Format: | Thesis |
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Curtin University
2023
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| Online Access: | http://hdl.handle.net/20.500.11937/93644 |
| _version_ | 1848765760378241024 |
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| author | Singha, Tanmay |
| author_facet | Singha, Tanmay |
| author_sort | Singha, Tanmay |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This thesis focuses on developing lightweight semantic segmentation models tailored for resource-constrained applications, effectively balancing accuracy and computational efficiency. It introduces several novel concepts, including knowledge sharing, dense bottleneck, and feature re-usability, which enhance the feature hierarchy by capturing fine-grained details, long-range dependencies, and diverse geometrical objects within the scene. To achieve precise object localization and improved semantic representations in real-time environments, the thesis introduces multi-stage feature aggregation, feature scaling, and hybrid-path attention methods. |
| first_indexed | 2025-11-14T11:40:22Z |
| format | Thesis |
| id | curtin-20.500.11937-93644 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:40:22Z |
| publishDate | 2023 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-936442023-10-31T01:26:45Z Efficient Semantic Segmentation for Resource-Constrained Applications with Lightweight Neural Networks Singha, Tanmay This thesis focuses on developing lightweight semantic segmentation models tailored for resource-constrained applications, effectively balancing accuracy and computational efficiency. It introduces several novel concepts, including knowledge sharing, dense bottleneck, and feature re-usability, which enhance the feature hierarchy by capturing fine-grained details, long-range dependencies, and diverse geometrical objects within the scene. To achieve precise object localization and improved semantic representations in real-time environments, the thesis introduces multi-stage feature aggregation, feature scaling, and hybrid-path attention methods. 2023 Thesis http://hdl.handle.net/20.500.11937/93644 Curtin University fulltext |
| spellingShingle | Singha, Tanmay Efficient Semantic Segmentation for Resource-Constrained Applications with Lightweight Neural Networks |
| title | Efficient Semantic Segmentation for Resource-Constrained
Applications with Lightweight Neural Networks |
| title_full | Efficient Semantic Segmentation for Resource-Constrained
Applications with Lightweight Neural Networks |
| title_fullStr | Efficient Semantic Segmentation for Resource-Constrained
Applications with Lightweight Neural Networks |
| title_full_unstemmed | Efficient Semantic Segmentation for Resource-Constrained
Applications with Lightweight Neural Networks |
| title_short | Efficient Semantic Segmentation for Resource-Constrained
Applications with Lightweight Neural Networks |
| title_sort | efficient semantic segmentation for resource-constrained
applications with lightweight neural networks |
| url | http://hdl.handle.net/20.500.11937/93644 |