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Multimodal Data Curation via Object Detection and Filter Ensembles
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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.
Forward citations
Cited by 2 Pith papers
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EcoDatum filters web image-text data by ensembling eight unimodal and multimodal quality scorers with weak-supervision weighting, reporting a DataComp small-scale average score of 0.182.
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