5 papers
Rigel: Self-Distilled Score Adaptation for Image and Video Captioning Evaluation
Shuitsu Koyama, Kazuki Matsuda, Yuiga Wada +3
Automatic evaluation of image and video captioning is essential for benchmarking multimodal systems, although standard evaluation metrics show limited alignment with human judgment…
ZINA: Multimodal Fine-grained Hallucination Detection and Editing
Yuiga Wada, Kazuki Matsuda, Komei Sugiura +1
Multimodal Large Language Models (MLLMs) often generate hallucinations, where the output deviates from the visual content. Given that these hallucinations can take diverse forms, d…
LLM-Free Image Captioning Evaluation in Reference-Flexible Settings
Shinnosuke Hirano, Yuiga Wada, Kazuki Matsuda +2
We focus on the automatic evaluation of image captions in both reference-based and reference-free settings. Existing metrics based on large language models (LLMs) favor their own g…
VELA: An LLM-Hybrid-as-a-Judge Approach for Evaluating Long Image Captions
Kazuki Matsuda, Yuiga Wada, Shinnosuke Hirano +2
In this study, we focus on the automatic evaluation of long and detailed image captions generated by multimodal Large Language Models (MLLMs). Most existing automatic evaluation me…
DENEB: A Hallucination-Robust Automatic Evaluation Metric for Image Captioning
Kazuki Matsuda, Yuiga Wada, Komei Sugiura
In this work, we address the challenge of developing automatic evaluation metrics for image captioning, with a particular focus on robustness against hallucinations. Existing metri…