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.
@article{arxiv.2401.12225,
title = {Multimodal Data Curation via Object Detection and Filter Ensembles},
author = {Tzu-Heng Huang and Changho Shin and Sui Jiet Tay and Dyah Adila and Frederic Sala},
journal= {arXiv preprint arXiv:2401.12225},
year = {2024}
}
Comments
Appeared in the Workshop of Towards the Next Generation of Computer Vision Datasets (TNGCV) on ICCV 2023