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

PDF Viewer

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (54)

[1]
TrafficInternVL: Spatially-Guided Fine-Tuning with Caption Refinement for Fine-Grained Traffic Safety Captioning and Visual Question Answering
2025Sasin Phimsiri, Sarut Sunpawatr et al.
[2]
CapRL: Stimulating Dense Image Caption Capabilities via Reinforcement Learning
2025Long Xing, Xiao-wen Dong et al.
[3]
InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
2025Weiyun Wang, Zhangwei Gao et al.
[4]
We-Math 2.0: A Versatile MathBook System for Incentivizing Visual Mathematical Reasoning
2025Runqi Qiao, Qiuna Tan et al.
[5]
Qwen3 Technical Report
2025An Yang, Anfeng Li et al.
[6]
DAPO: An Open-Source LLM Reinforcement Learning System at Scale
2025Qiying Yu, Zheng Zhang et al.
[7]
CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era
2025Kanzhi Cheng, Wenpo Song et al.
[8]
Painting with Words: Elevating Detailed Image Captioning with Benchmark and Alignment Learning
2025Qinghao Ye, Xianhan Zeng et al.
[9]
Qwen2.5-VL Technical Report
2025Shuai Bai, Keqin Chen et al.
[10]
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
2025Adam Suma, Samuel Dauncey
[11]
Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage
2024Saehyung Lee, Seunghyun Yoon et al.
[12]
Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis
2024Davide Bucciarelli, Nicholas Moratelli et al.
[13]
Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models
2024Zhengfeng Lai, Vasileios Saveris et al.
[14]
HybridFlow: A Flexible and Efficient RLHF Framework
2024Guangming Sheng, Chi Zhang et al.
[15]
MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark
2024Xiang Yue, Tianyu Zheng et al.
[16]
CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs
2024Zirui Wang, Mengzhou Xia et al.
[17]
Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs
2024Yu Qiao, Haodong Duan et al.
[18]
Do More Details Always Introduce More Hallucinations in LVLM-based Image Captioning?
2024Mingqian Feng, Yunlong Tang et al.
[19]
Are We on the Right Way for Evaluating Large Vision-Language Models?
2024Lin Chen, Jinsong Li et al.
[20]
MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?
2024Renrui Zhang, Dongzhi Jiang et al.

Showing 20 of 54 references

Founder's Pitch

"Develop enhanced image captioning models using dual-reward reinforcement learning for more complete and correct descriptions."

Image CaptioningScore: 6View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

3/4 signals

7.5

Series A Potential

3/4 signals

7.5

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 2/25/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.

Related Papers

Loading…