# Rigel: Self-Distilled Score Adaptation for Image and Video Captioning Evaluation

arXiv: https://arxiv.org/abs/2606.29997 · HTML: https://mlsanity.com/paper/2606.29997/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 47 references that are in the map.
- First posted on arXiv 29 Jun 2026.

## Builds on (most cited first, 30 of 31)

- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [SPICE: Semantic Propositional Image Caption Evaluation](https://mlsanity.com/paper/1607.08822/) (2016)
- [LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training](https://mlsanity.com/paper/2509.23661/) (2025)
- [AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark](https://mlsanity.com/paper/2410.03051/) (2024)
- [TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuning](https://mlsanity.com/paper/2410.19702/) (2024)
- [FOIL it! Find One mismatch between Image and Language caption](https://mlsanity.com/paper/1705.01359/) (2017)
- [Positive-Augmented Contrastive Learning for Image and Video Captioning Evaluation](https://mlsanity.com/paper/2303.12112/) (2023)
- [Polos: Multimodal Metric Learning from Human Feedback for Image Captioning](https://mlsanity.com/paper/2402.18091/) (2024)
- [Transparent Human Evaluation for Image Captioning](https://mlsanity.com/paper/2111.08940/) (2021)
- [UMIC: An Unreferenced Metric for Image Captioning via Contrastive Learning](https://mlsanity.com/paper/2106.14019/) (2021)
- [EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching](https://mlsanity.com/paper/2111.08919/) (2021)
- [From Images to Sentences through Scene Description Graphs using Commonsense Reasoning and Knowledge](https://mlsanity.com/paper/1511.03292/) (2015)
- [MeaCap: Memory-Augmented Zero-shot Image Captioning](https://mlsanity.com/paper/2403.03715/) (2024)
- [G-VEval: A Versatile Metric for Evaluating Image and Video Captions Using GPT-4o](https://mlsanity.com/paper/2412.13647/) (2024)
- [CIDEr-R: Robust Consensus-based Image Description Evaluation](https://mlsanity.com/paper/2109.13701/) (2021)
- [Models See Hallucinations: Evaluating the Factuality in Video Captioning](https://mlsanity.com/paper/2303.02961/) (2023)
- [Positive-Augmented Contrastive Learning for Vision-and-Language Evaluation and Training](https://mlsanity.com/paper/2410.07336/) (2024)
- [BRIDGE: Bridging Gaps in Image Captioning Evaluation with Stronger Visual Cues](https://mlsanity.com/paper/2407.20341/) (2024)
- [HICEScore: A Hierarchical Metric for Image Captioning Evaluation](https://mlsanity.com/paper/2407.18589/) (2024)
- [DENEB: A Hallucination-Robust Automatic Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2409.19255/) (2024)
- [EXPERT: An Explainable Image Captioning Evaluation Metric with Structured Explanations](https://mlsanity.com/paper/2506.24016/) (2025)
- [JaSPICE: Automatic Evaluation Metric Using Predicate-Argument Structures for Image Captioning Models](https://mlsanity.com/paper/2311.04192/) (2023)
- [CaptionSmiths: Flexibly Controlling Language Pattern in Image Captioning](https://mlsanity.com/paper/2507.01409/) (2025)
- [VELA: An LLM-Hybrid-as-a-Judge Approach for Evaluating Long Image Captions](https://mlsanity.com/paper/2509.25818/) (2025)
- [DISCODE: Distribution-Aware Score Decoder for Robust Automatic Evaluation of Image Captioning](https://mlsanity.com/paper/2512.14420/) (2025)

## Built on by (most cited first, 1 of 1)

- [VisExMEM: Visual Evidence-Based Explainable Multimodal Evaluation Metric](https://mlsanity.com/paper/2610.16441/) (2026)

## Abstract (arXiv)

Automatic evaluation of image and video captioning is essential for benchmarking multimodal systems, although standard evaluation metrics show limited alignment with human judgments. Recent approaches using large language models (LLMs), commonly referred to as LLM-as-a-Judge, have improved alignment with human judgments but still suffer from a mismatch between large-vocabulary language modeling and evaluation over a small label set. To address this, we propose Rigel, an automatic evaluation metric for image and video captioning, based on self-distilled score adaptation. The metric employs an evaluation-specific scoring head distilled from a frozen LLM, which captures judgment signals in a task-aligned space without relying on large-vocabulary token sets. We then refine the LLM backbone with human judgment data. To train Rigel, we constructed the Vid-Lepus dataset, which contains 3,338 video clips, 33,380 reference captions, and 5,637 candidate captions. Experiments on multiple benchmarks show that Rigel outperforms state-of-the-art metrics, achieving over 10-point improvements on ActivityNet-Fact in the reference-free setting.