  Contemporary large-scale visual language models (VLMs) exhibit strong
representation capacities, making them ubiquitous for enhancing image and text
understanding tasks. They are often trained in a contrastive manner on a large
and diverse corpus of images and corresponding text captions scraped from the
internet. Despite this, VLMs often struggle with compositional reasoning tasks
which require a fine-grained understanding of the complex interactions of
objects and their attributes. This failure can be attributed to two main
factors: 1) Contrastive approaches have traditionally focused on mining
negative examples from existing datasets. However, the mined negative examples
might not be difficult for the model to discriminate from the positive. An
alternative to mining would be negative sample generation 2) But existing
generative approaches primarily focus on generating hard negative texts
associated with a given image. Mining in the other direction, i.e., generating
negative image samples associated with a given text has been ignored. To
overcome both these limitations, we propose a framework that not only mines in
both directions but also generates challenging negative samples in both
modalities, i.e., images and texts. Leveraging these generative hard negative
samples, we significantly enhance VLMs' performance in tasks involving
multimodal compositional reasoning. Our code and dataset are released at
https://ugorsahin.github.io/enhancing-multimodal-compositional-reasoning-of-vlm.html.
