  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.
