arXiv Computer Vision
Sep 2

A Glance Is All You Need: Single-Pass Fine-Grained Image Captioning with SimLoss

arXiv:2609.00591v1 Announce Type: new Abstract: An image may be worth a thousand words, but most captioning models describe it in only a few. Modern vision-language models produce fluent high-level c...

By Suryaansh Jain, Rahasya Barkur, Vishal G, Ryan Rossi, Franck Dernoncourt, Jack Wang, Koustava Goswami, Nedim Lipka, Puneet Mathur, Samyadeep Basu, Seunghyun Yoon
arXiv Computer Vision
Aug 27

V-Rubrics: Visual Faithfulness via Rubric-Based Reinforcement Learning

The paper introduces V‑Rubrics, a reinforcement‑learning framework that evaluates vision‑language model responses by breaking them into atomic propositions and scoring them on Visual Faithfulness, Reasoning Consistency, and Instruction Following. Using a fine‑tuned Qwen3‑VL‑8B‑Instruct model and a newly created 50K‑example V‑Rubrics dataset, the authors demonstrate that rubric‑based GRPO outperforms both a shared SFT baseline and an answer‑only GRPO, especially on knowledge‑oriented and visually grounded reasoning tasks.

By Shulin Tian, Minglun Li, Yuhao Dong, Hao Ding, Jiarui Yao, Haiwen Diao, Jingkang Yang, Hongyuan Zhu, Ziwei Liu
arXiv AI
Aug 24

Re$^3$Cap: Retrieval-Guided Refinement for Image Captioning Enhancement via Reinforcement Learning

Re$^3$Cap introduces a retrieval‑guided refinement strategy for image captioning that leverages multi‑modal retrieval as a reasoning signal. The method, built on a Caption Refinement Suggester and a Caption Quality Assessor, detects hallucinations and omissions to produce more accurate and detailed captions without extra annotations. Experiments show it surpasses supervised fine‑tuning and improves relation reasoning by 8.64% on the COCO‑LN500 benchmark.

By Haonan Jia, Shichao Dong, Zenghui Sun, Jiawen Zheng, Ziqi Miao, Gege Shi, Qiuyu Zhao, Jinsong Lan, Xiaoyong Zhu, Bo Zheng
Hugging Face Trending Papers
Jul 6

Claim-Level Rubric Rewards for Video Caption Reinforcement Learning

In this paper, we introduce Claim-Level Rubric Rewards (CuRe), a structured reward framework designed to address the reward-design bottleneck in reinforcement learning for dense video captioning. Existing reward designs generally fall into two categories: holistic response-level judgment across heterogeneous criteria, or alignment-based evaluation against reference captions.

arXiv AI
Aug 3

Adjudicated Captioning: Multi-Agent Alignment Scoring and Consensus-Distilled Beam Arbitration for Strict Zero-Shot Image Captioning

arXiv:2607. 28986v1 Announce Type: cross Abstract: Zero-shot image captioning (ZIC) describes images without paired image-caption supervision during captioner training, relying on text-only corpora and frozen pretrained image-text scorers.

By Duy Tran Thanh, Thien-Phuc Doan, Long Nguyen-Vu, Ngo Tan Vu Khanh