Multimodal Data Curation via Object Detection and Filter Ensembles
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910305788887040 |
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| author | Huang, Tzu-Heng Shin, Changho Tay, Sui Jiet Adila, Dyah Sala, Frederic |
| author_facet | Huang, Tzu-Heng Shin, Changho Tay, Sui Jiet Adila, Dyah Sala, Frederic |
| contents | We propose an approach for curating multimodal data that we used for our entry in the 2023 DataComp competition filtering track. Our technique combines object detection and weak supervision-based ensembling. In the first of two steps in our approach, we employ an out-of-the-box zero-shot object detection model to extract granular information and produce a variety of filter designs. In the second step, we employ weak supervision to ensemble filtering rules. This approach results in a 4% performance improvement when compared to the best-performing baseline, producing the top-ranking position in the small scale track at the time of writing. Furthermore, in the medium scale track, we achieve a noteworthy 4.2% improvement over the baseline by simply ensembling existing baselines with weak supervision. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12225 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multimodal Data Curation via Object Detection and Filter Ensembles Huang, Tzu-Heng Shin, Changho Tay, Sui Jiet Adila, Dyah Sala, Frederic Computer Vision and Pattern Recognition Machine Learning We propose an approach for curating multimodal data that we used for our entry in the 2023 DataComp competition filtering track. Our technique combines object detection and weak supervision-based ensembling. In the first of two steps in our approach, we employ an out-of-the-box zero-shot object detection model to extract granular information and produce a variety of filter designs. In the second step, we employ weak supervision to ensemble filtering rules. This approach results in a 4% performance improvement when compared to the best-performing baseline, producing the top-ranking position in the small scale track at the time of writing. Furthermore, in the medium scale track, we achieve a noteworthy 4.2% improvement over the baseline by simply ensembling existing baselines with weak supervision. |
| title | Multimodal Data Curation via Object Detection and Filter Ensembles |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2401.12225 |