FLEUR: An Explainable Reference-Free Evaluation Metric for Image Captioning Using a Large Multimodal Model

• URL: http://arxiv.org/abs/2406.06004v1
• Date: Mon, 10 Jun 2024 03:57:39 GMT
• Title: FLEUR: An Explainable Reference-Free Evaluation Metric for Image Captioning Using a Large Multimodal Model
• Authors: Yebin Lee, Imseong Park, Myungjoo Kang,
• Abstract summary: We propose FLEUR, an explainable reference-free metric to introduce explainability into image captioning evaluation metrics. By leveraging a large multimodal model, FLEUR can evaluate the caption against the image without the need for reference captions. FLEUR achieves high correlations with human judgment across various image captioning evaluation benchmarks.
• Score: 5.330266804358638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most existing image captioning evaluation metrics focus on assigning a single numerical score to a caption by comparing it with reference captions. However, these methods do not provide an explanation for the assigned score. Moreover, reference captions are expensive to acquire. In this paper, we propose FLEUR, an explainable reference-free metric to introduce explainability into image captioning evaluation metrics. By leveraging a large multimodal model, FLEUR can evaluate the caption against the image without the need for reference captions, and provide the explanation for the assigned score. We introduce score smoothing to align as closely as possible with human judgment and to be robust to user-defined grading criteria. FLEUR achieves high correlations with human judgment across various image captioning evaluation benchmarks and reaches state-of-the-art results on Flickr8k-CF, COMPOSITE, and Pascal-50S within the domain of reference-free evaluation metrics. Our source code and results are publicly available at: https://github.com/Yebin46/FLEUR.

Related papers

• BRIDGE: Bridging Gaps in Image Captioning Evaluation with Stronger Visual Cues [47.213906345208315]
  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.
  arXiv  Detail & Related papers  (2024-07-29T18:00:17Z)
• InfoMetIC: An Informative Metric for Reference-free Image Caption Evaluation [69.1642316502563]
  We propose an Informative Metric for Reference-free Image Caption evaluation (InfoMetIC) Given an image and a caption, InfoMetIC is able to report incorrect words and unmentioned image regions at fine-grained level. We also construct a token-level evaluation dataset and demonstrate the effectiveness of InfoMetIC in fine-grained evaluation.
  arXiv  Detail & Related papers  (2023-05-10T09:22:44Z)
• Positive-Augmented Contrastive Learning for Image and Video Captioning Evaluation [47.40949434032489]
  We propose a new contrastive-based evaluation metric for image captioning, namely Positive-Augmented Contrastive learning Score (PAC-S) PAC-S unifies the learning of a contrastive visual-semantic space with the addition of generated images and text on curated data. Experiments spanning several datasets demonstrate that our new metric achieves the highest correlation with human judgments on both images and videos.
  arXiv  Detail & Related papers  (2023-03-21T18:03:14Z)
• EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching [90.98122161162644]
  Current metrics for video captioning are mostly based on the text-level comparison between reference and candidate captions. We propose EMScore (Embedding Matching-based score), a novel reference-free metric for video captioning. We exploit a well pre-trained vision-language model to extract visual and linguistic embeddings for computing EMScore.
  arXiv  Detail & Related papers  (2021-11-17T06:02:43Z)
• Contrastive Semantic Similarity Learning for Image Captioning Evaluation with Intrinsic Auto-encoder [52.42057181754076]
  Motivated by the auto-encoder mechanism and contrastive representation learning advances, we propose a learning-based metric for image captioning. We develop three progressive model structures to learn the sentence level representations. Experiment results show that our proposed method can align well with the scores generated from other contemporary metrics.
  arXiv  Detail & Related papers  (2021-06-29T12:27:05Z)
• CLIPScore: A Reference-free Evaluation Metric for Image Captioning [44.14502257230038]
  We show that CLIP, a cross-modal model pretrained on 400M image+caption pairs from the web, can be used for robust automatic evaluation of image captioning without the need for references. Experiments spanning several corpora demonstrate that our new reference-free metric, CLIPScore, achieves the highest correlation with human judgements. We also present a reference-augmented version, RefCLIPScore, which achieves even higher correlation.
  arXiv  Detail & Related papers  (2021-04-18T05:00:29Z)
• Intrinsic Image Captioning Evaluation [53.51379676690971]
  We propose a learning based metrics for image captioning, which we call Intrinsic Image Captioning Evaluation(I2CE) Experiment results show that our proposed method can keep robust performance and give more flexible scores to candidate captions when encountered with semantic similar expression or less aligned semantics.
  arXiv  Detail & Related papers  (2020-12-14T08:36:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.