Causal Graphical Models for Vision-Language Compositional Understanding
Fiorenzo Parascandolo Nicholas Moratelli Enver Sangineto Lorenzo Baraldi Rita Cucchiara
Recent work has empirically shown that Vision-Language Models (VLMs) struggle
to fully understand the compositional properties of the human language, usually
modeling an image caption as a “bag of words”. As a result, they perform
poorly on compositional tasks, which require a deeper understanding of the different
entities of a sentence (subject, verb, etc.) jointly with their mutual relationships
in order to be solved. In this paper, we model the dependency relations
among textual and visual tokens using a Causal Graphical Model (CGM), built using
a dependency parser, and we train a decoder conditioned by the VLM visual
encoder. Differently from standard autoregressive or parallel predictions, our decoder’s
generative process is partially-ordered following the CGM structure. This
structure encourages the decoder to learn only the main causal dependencies in
a sentence discarding spurious correlations. Using extensive experiments on five
compositional benchmarks, we show that our method significantly outperforms
all the state-of-the-art compositional approaches by a large margin, and it also improves
over methods trained using much larger datasets. 
Our model weights and code are publicly available.
Compositionality, Vision-Language Models, Causal Learning