BRIDGE: Bridging Gaps in Image Captioning Evaluation with Stronger Visual Cues

• URL: http://arxiv.org/abs/2407.20341v1
• Date: Mon, 29 Jul 2024 18:00:17 GMT
• Title: BRIDGE: Bridging Gaps in Image Captioning Evaluation with Stronger Visual Cues
• Authors: Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara,
• Abstract summary: We propose BRIDGE, a new learnable and reference-free image captioning metric. Our proposal achieves state-of-the-art results compared to existing reference-free evaluation scores.
• Score: 47.213906345208315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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