VCap: Hypergeometric Rewards for Weak-to-Strong Visual Captioning

• URL: http://arxiv.org/abs/2605.28023v1
• Date: Wed, 27 May 2026 06:27:04 GMT
• Title: VCap: Hypergeometric Rewards for Weak-to-Strong Visual Captioning
• Abstract summary: We propose VCap, a Witness-Adjudicator reward that pairs the reference caption (a witness) with the visual signal (an adjudicator)<n>VCap delivers a reward signal with hypergeometric-distribution-level precision for caption quality verification.<n>In our experiments, an 8B model trained with VCap outperforms open- and closed-source SOTA models on multiple image and video captioning benchmarks.
• Score: 57.588999592609646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual captioning requires models to capture visual content faithfully while minimizing both omission and hallucination. As the dominant paradigm for captioning, MLLMs have achieved strong performance through scaling and high-quality data. Recently, RL has emerged as a key route to driving MLLMs toward higher precision and broader coverage, however, existing reward designs for captioning fail to provide fine-grained and reliable signals for factual verification, limiting their effectiveness. To address this, we propose VCap, a Witness-Adjudicator reward that pairs the reference caption (a witness) with the visual signal (an adjudicator). By explicitly verifying factual consistency between the reference and policy-generated captions grounded in the visual signal, VCap delivers a reward signal with hypergeometric-distribution-level precision for caption quality verification. This design enables effective learning even from imperfect references, facilitating weak-to-strong generalization in RL training. In our experiments, an 8B model trained with VCap outperforms open- and closed-source SOTA models on multiple image and video captioning benchmarks. Human evaluation further confirms its strong alignment with factual correctness. Additionally, VCap improves MLLM perceptual capability, generalizes across tasks, and surpasses best-of-N distillation, challenging prior assumptions about RLVR.

Related papers

• CapRL++: Unified Reinforcement Learning with Verifiable Rewards for Dense Image and Video Captioning [86.9494763617273]
  We introduce Captioning Reinforcement Learning++ (CapRL++), a reference-free training framework that redefines caption quality through its utility.<n>We show that CapRL++ improves dense caption quality and strengthens caption-based pretraining across tasks such as spatial and temporal understanding.
  arXiv  Detail & Related papers  (2026-06-08T12:09:20Z)
• BalCapRL: A Balanced Framework for RL-Based MLLM Image Captioning [4.441850776689605]
  We propose a more balanced reinforcement learning framework that jointly optimize utility-aware correctness, reference coverage, and linguistic quality.<n>We show that our method consistently improves caption quality, with peak gains of +13.6 DCScore, +9.0 CaptionQA, and +29.0 Cap across different models.
  arXiv  Detail & Related papers  (2026-05-08T07:48:36Z)
• RubiCap: Rubric-Guided Reinforcement Learning for Dense Image Captioning [15.881702574415861]
  RubiCap is a novelReinforcement learning framework that derives fine-grained, sample-specific reward signals from rubrics.<n> RubiCap achieves the highest win rates on CapArena, outperforming supervised distillation, prior RL methods, human-expert annotations, and GPT-4V-augmented outputs.
  arXiv  Detail & Related papers  (2026-03-10T03:51:27Z)
• CCCaption: Dual-Reward Reinforcement Learning for Complete and Correct Image Captioning [23.289413412387223]
  We introduce CCCaption: a dual-reward reinforcement learning framework with a dedicated fine-tuning corpus.<n>For completeness, we use diverse LVLMs to disentangle the image into a set of visual queries, and reward captions that answer more of these queries.<n>For correctness, we penalize captions that contain hallucinations by validating the authenticity of sub-caption queries.
  arXiv  Detail & Related papers  (2026-02-25T07:34:26Z)
• CapRL: Stimulating Dense Image Caption Capabilities via Reinforcement Learning [90.19455861166745]
  We introduce Captioning Reinforcement Learning (CapRL), a training framework that redefines caption quality through its utility.<n>As the first study to apply RLVR to the subjective image captioning task, we demonstrate that CapRL significantly enhances multiple settings.<n>CapRL achieves performance comparable to Qwen2.5-VL-72B, while exceeding the baseline by an average margin of 8.4%.
  arXiv  Detail & Related papers  (2025-09-26T17:59:55Z)
• ScaleCap: Inference-Time Scalable Image Captioning via Dual-Modality Debiasing [128.8346376825612]
  Key challenges of high-quality image captioning lie in the inherent biases of LVLMs.<n>We propose a scalable debiased captioning strategy, which continuously enriches and calibrates the caption with increased inference budget.<n>Annotating 450K images with ScaleCap and using them for LVLM pretraining leads to consistent performance gains across 11 widely used benchmarks.
  arXiv  Detail & Related papers  (2025-06-24T17:59:55Z)
• Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage [50.84150600032693]
  Multimodal large language models (MLLMs) excel at generating highly detailed captions but often produce hallucinations.<n>We propose a multiagent approach that leverages LLM-MLLM collaboration to correct given captions.<n>Our proposed method significantly enhances the factual accuracy of captions, even improving those generated by GPT-4V.
  arXiv  Detail & Related papers  (2024-12-20T01:37:22Z)
• Prompt-based Learning for Unpaired Image Captioning [86.44188293709307]
  Unpaired Image Captioning (UIC) has been developed to learn image descriptions from unaligned vision-language sample pairs. Recent successes of Vision-Language Pre-Trained Models (VL-PTMs) have triggered the development of prompt-based learning. We present in this paper a novel scheme based on prompt to train the UIC model, making best use of the powerful generalization ability.
  arXiv  Detail & Related papers  (2022-05-26T03:13:43Z)

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.