No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916600577261568 |
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| author | Mededovic, Emil Laurentius, Valdy Wu, Yuli Kopaczka, Marcin Chen, Zhu Schulz, Mareike Tolba, René Stegmaier, Johannes |
| author_facet | Mededovic, Emil Laurentius, Valdy Wu, Yuli Kopaczka, Marcin Chen, Zhu Schulz, Mareike Tolba, René Stegmaier, Johannes |
| contents | We analyze the capabilities of foundation models addressing the tedious task of generating annotations for animal tracking. Annotating a large amount of data is vital and can be a make-or-break factor for the robustness of a tracking model. Robustness is particularly crucial in animal tracking, as accurate tracking over long time horizons is essential for capturing the behavior of animals. However, generating additional annotations using foundation models can be counterproductive, as the quality of the annotations is just as important. Poorly annotated data can introduce noise and inaccuracies, ultimately compromising the performance and accuracy of the trained model. Over-reliance on automated annotations without ensuring precision can lead to diminished results, making careful oversight and quality control essential in the annotation process. Ultimately, we demonstrate that a thoughtful combination of automated annotations and manually annotated data is a valuable strategy, yielding an IDF1 score of 80.8 against blind usage of SAM2 video with an IDF1 score of 65.6. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03907 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking Mededovic, Emil Laurentius, Valdy Wu, Yuli Kopaczka, Marcin Chen, Zhu Schulz, Mareike Tolba, René Stegmaier, Johannes Computer Vision and Pattern Recognition We analyze the capabilities of foundation models addressing the tedious task of generating annotations for animal tracking. Annotating a large amount of data is vital and can be a make-or-break factor for the robustness of a tracking model. Robustness is particularly crucial in animal tracking, as accurate tracking over long time horizons is essential for capturing the behavior of animals. However, generating additional annotations using foundation models can be counterproductive, as the quality of the annotations is just as important. Poorly annotated data can introduce noise and inaccuracies, ultimately compromising the performance and accuracy of the trained model. Over-reliance on automated annotations without ensuring precision can lead to diminished results, making careful oversight and quality control essential in the annotation process. Ultimately, we demonstrate that a thoughtful combination of automated annotations and manually annotated data is a valuable strategy, yielding an IDF1 score of 80.8 against blind usage of SAM2 video with an IDF1 score of 65.6. |
| title | No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.03907 |