ViLLA: Fine-Grained Vision-Language Representation Learning from
  Real-World Data
Maya Varma Jean-Benoit Delbrouck Sarah Hooper Akshay Chaudhari Curtis Langlotz
Vision-language models (VLMs), such as CLIP and ALIGN, are generally trained
on datasets consisting of image-caption pairs obtained from the web. However,
real-world multimodal datasets, such as healthcare data, are significantly more
complex: each image (e.g. X-ray) is often paired with text (e.g. physician
report) that describes many distinct attributes occurring in fine-grained
regions of the image. We refer to these samples as exhibiting high pairwise
complexity, since each image-text pair can be decomposed into a large number of
region-attribute pairings. The extent to which VLMs can capture fine-grained
relationships between image regions and textual attributes when trained on such
data has not been previously evaluated. The first key contribution of this work
is to demonstrate through systematic evaluations that as the pairwise
complexity of the training dataset increases, standard VLMs struggle to learn
region-attribute relationships, exhibiting performance degradations of up to
37% on retrieval tasks. In order to address this issue, we introduce ViLLA as
our second key contribution. ViLLA, which is trained to capture fine-grained
region-attribute relationships from complex datasets, involves two components:
(a) a lightweight, self-supervised mapping model to decompose image-text
samples into region-attribute pairs, and (b) a contrastive VLM to learn
representations from generated region-attribute pairs. We demonstrate with
experiments across four domains (synthetic, product, medical, and natural
images) that ViLLA outperforms comparable VLMs on fine-grained reasoning tasks,
such as zero-shot object detection (up to 3.6 AP50 points on COCO and 0.6 mAP
points on LVIS) and retrieval (up to 14.2 R-Precision points).