Weakly Supervised Vision-and-Language Pre-training with Relative
  Representations
Chi Chen Peng Li Maosong Sun Yang Liu
Weakly supervised vision-and-language pre-training (WVLP), which learns
cross-modal representations with limited cross-modal supervision, has been
shown to effectively reduce the data cost of pre-training while maintaining
decent performance on downstream tasks. However, current WVLP methods use only
local descriptions of images, i.e., object tags, as cross-modal anchors to
construct weakly-aligned image-text pairs for pre-training. This affects the
data quality and thus the effectiveness of pre-training. In this paper, we
propose to directly take a small number of aligned image-text pairs as anchors,
and represent each unaligned image and text by its similarities to these
anchors, i.e., relative representations. We build a WVLP framework based on the
relative representations, namely RELIT, which collects high-quality
weakly-aligned image-text pairs from large-scale image-only and text-only data
for pre-training through relative representation-based retrieval and
generation. Experiments on four downstream tasks show that RELIT achieves new
state-of-the-art results under the weakly supervised setting.