VIVECaption: A Split Approach to Caption Quality Improvement
Caption quality has emerged as a critical bottleneck in training high-quality text-to-image (T2I) and text-to-video (T2V) generative models. While visual language models (VLMs) are commonly deployed to generate captions from visual data, they suffer from hallucinations, poor compositional reasoning, and limited fine-grained understanding, resulting in misaligned image-caption pairs that degrade downstream model performance. This technical report introduces VIVECaption, a systematic two-sided approach to caption quality improvement. We first establish a comprehensive taxonomy of caption evaluation metrics, distinguishing between "universal" and "instance-grounded" metrics, with the ultimat...
| Authors | Varun Ananth, Baqiao Liu, Haoran Cai |
|---|---|
| Primary category | cs.CV |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
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