C-CLIP: Contrastive Image-Text Encoders to Close the
  Descriptive-Commentative Gap
William Theisen Walter Scheirer
The interplay between the image and comment on a social media post is one of
high importance for understanding its overall message. Recent strides in
multimodal embedding models, namely CLIP, have provided an avenue forward in
relating image and text. However the current training regime for CLIP models is
insufficient for matching content found on social media, regardless of site or
language. Current CLIP training data is based on what we call ``descriptive''
text: text in which an image is merely described. This is something rarely seen
on social media, where the vast majority of text content is ``commentative'' in
nature. The captions provide commentary and broader context related to the
image, rather than describing what is in it. Current CLIP models perform poorly
on retrieval tasks where image-caption pairs display a commentative
relationship. Closing this gap would be beneficial for several important
application areas related to social media. For instance, it would allow groups
focused on Open-Source Intelligence Operations (OSINT) to further aid efforts
during disaster events, such as the ongoing Russian invasion of Ukraine, by
easily exposing data to non-technical users for discovery and analysis. In
order to close this gap we demonstrate that training contrastive image-text
encoders on explicitly commentative pairs results in large improvements in
retrieval results, with the results extending across a variety of non-English
languages.