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.

我们提出了一种用于 2023 年 DataComp 竞赛过滤轨道的多模态数据筛选方法，该方法将目标检测和基于弱监督的集成相结合，使性能提升了 4%，并在小规模轨道中取得了排名第一的位置，并且通过集成现有基准和弱监督方法在中等规模轨道上实现了 4.2% 的改进。