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arXiv · 2401.12225

Multimodal Data Curation via Object Detection and Filter Ensembles

Tzu-Heng Huang·Changho Shin·Sui Jiet Tay·Dyah Adila·Frederic Sala
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Abstract

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.

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Tzu-Heng Huang, Changho Shin, Sui Jiet Tay, Dyah Adila, Frederic Sala. 2024-01-05. Multimodal Data Curation via Object Detection and Filter Ensembles. https://arxiv.org/abs/2401.12225

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