  Despite recent progress towards scaling up multimodal vision-language models,
these models are still known to struggle on compositional generalization
benchmarks such as Winoground. We find that a critical component lacking from
current vision-language models is relation-level alignment: the ability to
match directional semantic relations in text (e.g., "mug in grass") with
spatial relationships in the image (e.g., the position of the mug relative to
the grass). To tackle this problem, we show that relation alignment can be
enforced by encouraging the directed language attention from 'mug' to 'grass'
(capturing the semantic relation 'in') to match the directed visual attention
from the mug to the grass. Tokens and their corresponding objects are softly
identified using the cross-modal attention. We prove that this notion of soft
relation alignment is equivalent to enforcing congruence between vision and
language attention matrices under a 'change of basis' provided by the
cross-modal attention matrix. Intuitively, our approach projects visual
attention into the language attention space to calculate its divergence from
the actual language attention, and vice versa. We apply our Cross-modal
Attention Congruence Regularization (CACR) loss to UNITER and improve on the
state-of-the-art approach to Winoground.
