Machine learning assisted annotation leveraging large data foundation models
This research paper explores the potential of machine learning-assisted annotation methods in streamlining the data annotation process, enhancing annotation quality, and unlocking the full potential of annotated datasets at a production level. The author investigates the use of various advanced mach...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175707 |
| _version_ | 1811680137874243584 |
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| author | Tey, Chin Yi |
| author2 | Lin Guosheng |
| author_facet | Lin Guosheng Tey, Chin Yi |
| author_sort | Tey, Chin Yi |
| collection | NTU |
| description | This research paper explores the potential of machine learning-assisted annotation methods in streamlining the data annotation process, enhancing annotation quality, and unlocking the full potential of annotated datasets at a production level. The author investigates the use of various advanced machine learning models, including the Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation (SEAM), Sobel Image Edge Detection, and the Segment Anything Model (SAM), to address challenges in large-scale data annotation. The study demonstrates that machine learning-assisted annotation methods represent a crucial solution to bridge the gaps in SAM, forming the basis for future ML-assisted annotation. The research highlights the significance of continued innovation in machine learning and computer vision to advance state-of-the-art data annotation practices and methodologies. |
| first_indexed | 2024-10-01T03:20:17Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/175707 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:20:17Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1757072024-05-03T15:38:43Z Machine learning assisted annotation leveraging large data foundation models Tey, Chin Yi Lin Guosheng School of Computer Science and Engineering gslin@ntu.edu.sg Computer and Information Science Machine learning Data annotation Semantic segmentation Computer vision Deep learning models This research paper explores the potential of machine learning-assisted annotation methods in streamlining the data annotation process, enhancing annotation quality, and unlocking the full potential of annotated datasets at a production level. The author investigates the use of various advanced machine learning models, including the Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation (SEAM), Sobel Image Edge Detection, and the Segment Anything Model (SAM), to address challenges in large-scale data annotation. The study demonstrates that machine learning-assisted annotation methods represent a crucial solution to bridge the gaps in SAM, forming the basis for future ML-assisted annotation. The research highlights the significance of continued innovation in machine learning and computer vision to advance state-of-the-art data annotation practices and methodologies. Bachelor's degree 2024-05-03T07:39:55Z 2024-05-03T07:39:55Z 2024 Final Year Project (FYP) Tey, C. Y. (2024). Machine learning assisted annotation leveraging large data foundation models. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175707 https://hdl.handle.net/10356/175707 en SCSE23-0335 application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science Machine learning Data annotation Semantic segmentation Computer vision Deep learning models Tey, Chin Yi Machine learning assisted annotation leveraging large data foundation models |
| title | Machine learning assisted annotation leveraging large data foundation models |
| title_full | Machine learning assisted annotation leveraging large data foundation models |
| title_fullStr | Machine learning assisted annotation leveraging large data foundation models |
| title_full_unstemmed | Machine learning assisted annotation leveraging large data foundation models |
| title_short | Machine learning assisted annotation leveraging large data foundation models |
| title_sort | machine learning assisted annotation leveraging large data foundation models |
| topic | Computer and Information Science Machine learning Data annotation Semantic segmentation Computer vision Deep learning models |
| url | https://hdl.handle.net/10356/175707 |
| work_keys_str_mv | AT teychinyi machinelearningassistedannotationleveraginglargedatafoundationmodels |