Papers › Mutual Information Divergence: A Unified Metric for Multimodal Generative Models

Mutual Information Divergence: A Unified Metric for Multimodal Generative Models

25 May 2022arXiv:2205.13445archive 2025-07-28

Jin-Hwa Kim, Yunji Kim, Jiyoung Lee, Kang Min Yoo, Sang-Woo Lee

Text-to-image generation and image captioning are recently emerged as a new experimental paradigm to assess machine intelligence. They predict continuous quantity accompanied by their sampling techniques in the generation, making evaluation complicated and intractable to get marginal distributions. Based on a recent trend that multimodal generative evaluations exploit a vison-and-language pre-trained model, we propose the negative Gaussian cross-mutual information using the CLIP features as a unified metric, coined by Mutual Information Divergence (MID). To validate, we extensively compare it with competing metrics using carefully-generated or human-annotated judgments in text-to-image generation and image captioning tasks. The proposed MID significantly outperforms the competitive methods by having consistency across benchmarks, sample parsimony, and robustness toward the exploited CLIP model. We look forward to seeing the underrepresented implications of the Gaussian cross-mutual information in multimodal representation learning and the future works based on this novel proposition.

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naver-ai/mid.metric officialmentioned on GitHubpytorch report

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Tasks

Human Judgment ClassificationHuman Judgment CorrelationImage CaptioningImage GenerationRepresentation LearningText to Image GenerationText-to-Image Generation

2 archive task tags without a task page not shown.

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Human Judgment Classification Pascal-50S MID Mean Accuracy 85.2 #1 of 3 Archive leaderboard report
Human Judgment Correlation Flickr8k-CF MID Kendall's Tau-b 37.3 #1 of 3 Archive leaderboard report
Human Judgment Correlation Flickr8k-Expert MID Kendall's Tau-c 54.9 #1 of 4 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

CLIP

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