CCCaption: Dual-Reward Reinforcement Learning for Complete and Correct Image Captioning

• URL: http://arxiv.org/abs/2602.21655v1
• Date: Wed, 25 Feb 2026 07:34:26 GMT
• Title: CCCaption: Dual-Reward Reinforcement Learning for Complete and Correct Image Captioning
• Authors: Zhijiang Tang, Linhua Wang, Jiaxin Qi, Weihao Jiang, Peng Hou, Anxiang Zeng, Jianqiang Huang,
• Abstract summary: 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.
• Score: 23.289413412387223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image captioning remains a fundamental task for vision language understanding, yet ground-truth supervision still relies predominantly on human-annotated references. Because human annotations reflect subjective preferences and expertise, ground-truth captions are often incomplete or even incorrect, which in turn limits caption models. We argue that caption quality should be assessed by two objective aspects: completeness (does the caption cover all salient visual facts?) and correctness (are the descriptions true with respect to the image?). To this end, we introduce CCCaption: a dual-reward reinforcement learning framework with a dedicated fine-tuning corpus that explicitly optimizes these properties to generate \textbf{C}omplete and \textbf{C}orrect \textbf{Captions}. 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, with a dynamic query sampling strategy to improve training efficiency. For correctness, we penalize captions that contain hallucinations by validating the authenticity of sub-caption queries, which are derived from the caption decomposition. Our symmetric dual-reward optimization jointly maximizes completeness and correctness, guiding models toward captions that better satisfy these objective criteria. Extensive experiments across standard captioning benchmarks show consistent improvements, offering a principled path to training caption models beyond human-annotation imitation.

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