  Large web-sourced multimodal datasets have powered a slew of new methods for
learning general-purpose visual representations, advancing the state of the art
in computer vision and revolutionizing zero- and few-shot recognition. One
crucial decision facing practitioners is how, if at all, to curate these
ever-larger datasets. For example, the creators of the LAION-5B dataset chose
to retain only image-caption pairs whose CLIP similarity score exceeded a
designated threshold. In this paper, we propose a new state-of-the-art data
filtering approach motivated by our observation that nearly 40% of LAION's
images contain text that overlaps significantly with the caption. Intuitively,
such data could be wasteful as it incentivizes models to perform optical
character recognition rather than learning visual features. However, naively
removing all such data could also be wasteful, as it throws away images that
contain visual features (in addition to overlapping text). Our simple and
scalable approach, T-MARS (Text Masking and Re-Scoring), filters out only those
pairs where the text dominates the remaining visual features -- by first
masking out the text and then filtering out those with a low CLIP similarity
score of the masked image. Experimentally, T-MARS outperforms the top-ranked
method on the "medium scale" of DataComp (a data filtering benchmark) by a
margin of 6.5% on ImageNet and 4.7% on VTAB. Additionally, our systematic
evaluation on various data pool sizes from 2M to 64M shows that the accuracy
gains enjoyed by T-MARS linearly increase as data and compute are scaled
exponentially. Code is available at https://github.com/locuslab/T-MARS.
