  Effectively aligning with human judgment when evaluating machine-generated
image captions represents a complex yet intriguing challenge. Existing
evaluation metrics like CIDEr or CLIP-Score fall short in this regard as they
do not take into account the corresponding image or lack the capability of
encoding fine-grained details and penalizing hallucinations. To overcome these
issues, in this paper, we propose BRIDGE, a new learnable and reference-free
image captioning metric that employs a novel module to map visual features into
dense vectors and integrates them into multi-modal pseudo-captions which are
built during the evaluation process. This approach results in a multimodal
metric that properly incorporates information from the input image without
relying on reference captions, bridging the gap between human judgment and
machine-generated image captions. Experiments spanning several datasets
demonstrate that our proposal achieves state-of-the-art results compared to
existing reference-free evaluation scores. Our source code and trained models
are publicly available at: https://github.com/aimagelab/bridge-score.
