  Pre-trained representations are becoming crucial for many NLP and perception
tasks. While representation learning in NLP has transitioned to training on raw
text without human annotations, visual and vision-language representations
still rely heavily on curated training datasets that are expensive or require
expert knowledge. For vision applications, representations are mostly learned
using datasets with explicit class labels such as ImageNet or OpenImages. For
vision-language, popular datasets like Conceptual Captions, MSCOCO, or CLIP all
involve a non-trivial data collection (and cleaning) process. This costly
curation process limits the size of datasets and hence hinders the scaling of
trained models. In this paper, we leverage a noisy dataset of over one billion
image alt-text pairs, obtained without expensive filtering or post-processing
steps in the Conceptual Captions dataset. A simple dual-encoder architecture
learns to align visual and language representations of the image and text pairs
using a contrastive loss. We show that the scale of our corpus can make up for
its noise and leads to state-of-the-art representations even with such a simple
learning scheme. Our visual representation achieves strong performance when
transferred to classification tasks such as ImageNet and VTAB. The aligned
visual and language representations enables zero-shot image classification and
also set new state-of-the-art results on Flickr30K and MSCOCO image-text
retrieval benchmarks, even when compared with more sophisticated
cross-attention models. The representations also enable cross-modality search
with complex text and text + image queries.
