mlsanityData to 14 Oct 2026

TIGEr: Text-to-Image Grounding for Image Caption Evaluation

arXiv 1909.02050 ↗First posted 4 Sep 2019cs.CL cs.CVcited by 58

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Abstract (arXiv)

This paper presents a new metric called TIGEr for the automatic evaluation of image captioning systems. Popular metrics, such as BLEU and CIDEr, are based solely on text matching between reference captions and machine-generated captions, potentially leading to biased evaluations because references may not fully cover the image content and natural language is inherently ambiguous. Building upon a machine-learned text-image grounding model, TIGEr allows to evaluate caption quality not only based on how well a caption represents image content, but also on how well machine-generated captions match human-generated captions. Our empirical tests show that TIGEr has a higher consistency with human judgments than alternative existing metrics. We also comprehensively assess the metric's effectiveness in caption evaluation by measuring the correlation between human judgments and metric scores.

Key facts. Cited by 58 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Sep 2019.

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Citing papers per year: 2021: 9; 2022: 6; 2023: 10; 2024: 18; 2025: 12; 2026: 3.

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  1. 2021CLIPScore: A Reference-free Evaluation Metric for Image Captioningcited by 2,424
  2. 2021From Show to Tell: A Survey on Deep Learning-based Image Captioningcited by 138
  3. 2024Playground v3: Improving Text-to-Image Alignment with Deep-Fusion Large Language Modelscited by 116
  4. 2023Positive-Augmented Contrastive Learning for Image and Video Captioning Evaluationcited by 78
  5. 2021Perturbation CheckLists for Evaluating NLG Evaluation Metricscited by 50
  6. 2024Polos: Multimodal Metric Learning from Human Feedback for Image Captioningcited by 46
  7. 2024FLEUR: An Explainable Reference-Free Evaluation Metric for Image Captioning Using a Large Multimodal Modelcited by 45
  8. 2023CLAIR: Evaluating Image Captions with Large Language Modelscited by 43
  9. 2021Injecting Semantic Concepts into End-to-End Image Captioningcited by 41
  10. 2023See, Say, and Segment: Teaching LMMs to Overcome False Premisescited by 41
  11. 2021EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matchingcited by 36
  12. 2022Mutual Information Divergence: A Unified Metric for Multimodal Generative Modelscited by 35
38 more
  1. 2024AutoAD III: The Prequel -- Back to the Pixelscited by 35
  2. 2021Can Audio Captions Be Evaluated with Image Caption Metrics?cited by 33
  3. 2023InfoMetIC: An Informative Metric for Reference-free Image Caption Evaluationcited by 25
  4. 2024Neptune: The Long Orbit to Benchmarking Long Video Understandingcited by 25
  5. 2023Balance Act: Mitigating Hubness in Cross-Modal Retrieval with Query and Gallery Bankscited by 20
  6. 2021SMURF: SeMantic and linguistic UndeRstanding Fusion for Caption Evaluation via Typicality Analysiscited by 18
  7. 2024A Survey on Quality Metrics for Text-to-Image Generationcited by 18
  8. 2025Image Captioning Evaluation in the Age of Multimodal LLMs: Challenges and Future Perspectivescited by 18
  9. 2024Positive-Augmented Contrastive Learning for Vision-and-Language Evaluation and Trainingcited by 16
  10. 2024MetaMetrics: Calibrating Metrics For Generation Tasks Using Human Preferencescited by 15
  11. 2023Test-Time Distribution Normalization for Contrastively Learned Vision-language Modelscited by 14
  12. 2024BRIDGE: Bridging Gaps in Image Captioning Evaluation with Stronger Visual Cuescited by 13
  13. 2024HICEScore: A Hierarchical Metric for Image Captioning Evaluationcited by 12
  14. 2024DENEB: A Hallucination-Robust Automatic Evaluation Metric for Image Captioningcited by 11
  15. 2024Image2Text2Image: A Novel Framework for Label-Free Evaluation of Image-to-Text Generation with Text-to-Image Diffusion Modelscited by 10
  16. 2024Text-to-Image Synthesis: A Decade Surveycited by 9
  17. 2025VCapsBench: A Large-scale Fine-grained Benchmark for Video Caption Quality Evaluationcited by 9
  18. 2022RoViST:Learning Robust Metrics for Visual Storytellingcited by 7
  19. 2024Vision Language Model-based Caption Evaluation Method Leveraging Visual Context Extractioncited by 7
  20. 2025EXPERT: An Explainable Image Captioning Evaluation Metric with Structured Explanationscited by 7
  21. 2025Med-GLIP: Advancing Medical Language-Image Pre-training with Large-scale Grounded Datasetcited by 7
  22. 2021ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generationcited by 6
  23. 2022What's in a Caption? Dataset-Specific Linguistic Diversity and Its Effect on Visual Description Models and Metricscited by 6
  24. 2022Distribution Aware Metrics for Conditional Natural Language Generationcited by 6
  25. 2024Large Language Models Are Active Critics in NLG Evaluationcited by 6
  26. 2025Adding Chocolate to Mint: Mitigating Metric Interference in Machine Translationcited by 6
  27. 2022Switching to Discriminative Image Captioning by Relieving a Bottleneck of Reinforcement Learningcited by 4
  28. 2024Cobra Effect in Reference-Free Image Captioning Metricscited by 4
  29. 2025VC-Inspector: Advancing Reference-free Evaluation of Video Captions with Factual Analysiscited by 4
  30. 2023Self-Supervised Image Captioning with CLIPcited by 3
  31. 2023The Challenges of Image Generation Models in Generating Multi-Component Imagescited by 3
  32. 2024Multi-modal, Multi-task, Multi-criteria Automatic Evaluation with Vision Language Modelscited by 3
  33. 2025"It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Modelscited by 3
  34. 2022Are metrics measuring what they should? An evaluation of image captioning task metricscited by 2
  35. 2023A request for clarity over the End of Sequence token in the Self-Critical Sequence Trainingcited by 2
  36. 2025L-CLIPScore: a Lightweight Embedding-based Captioning Metric for Evaluating and Trainingcited by 2
  37. 2025DISCODE: Distribution-Aware Score Decoder for Robust Automatic Evaluation of Image Captioningcited by 2
  38. 2026Do Image-Text Metrics Respect Semantic Invariances?cited by 2

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