Multimodal Data Curation via Object Detection and Filter Ensembles
Tzu-Heng Huang Changho Shin Sui Jiet Tay Dyah Adila Frederic Sala
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.