Summary of Bridge: Bridging Gaps in Image Captioning Evaluation with Stronger Visual Cues, by Sara Sarto et al.
BRIDGE: Bridging Gaps in Image Captioning Evaluation with Stronger Visual Cues
by Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new evaluation metric, called BRIDGE, to assess the quality of machine-generated image captions. The existing metrics, such as CIDEr or CLIP-Score, have limitations in capturing fine-grained details and penalizing hallucinations. To address these issues, the authors introduce a novel module that maps visual features into dense vectors and integrates them into multi-modal pseudo-captions built during the evaluation process. This approach allows for a multimodal metric that incorporates information from the input image without relying on reference captions. The proposed metric achieves state-of-the-art results compared to existing reference-free evaluation scores across several datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand how well a computer-generated caption matches a picture. Right now, there’s no great way to measure this. Some methods don’t take into account what the picture looks like, while others rely on having perfect captions to compare with. This paper introduces a new approach called BRIDGE that fixes these problems. It creates a special kind of fake caption that takes into account both the picture and what it’s supposed to describe. The results are impressive, showing that this method is better than other ways of evaluating computer-generated captions. |
Keywords
» Artificial intelligence » Multi modal
