We introduce FlexCap, a vision-language model that generates region-specific descriptions of varying lengths. FlexCap is trained to produce length-conditioned captions for input boxes, enabling control over information density, with descriptions ranging from concise object labels to detailed captions. To achieve this, we create large-scale training datasets of image region descriptions with varying lengths from captioned web images. We demonstrate FlexCap's effectiveness in several applications: first, it achieves strong performance in dense captioning tasks on the Visual Genome dataset. Second, we show how FlexCap's localized descriptions can serve as input to a large language model to create a visual question answering (VQA) system, achieving state-of-the-art zero-shot performance on multiple VQA benchmarks. Our experiments illustrate FlexCap's utility for tasks including image labeling, object attribute recognition, and visual dialog. Project webpage: https://flex-cap.github.io .
@article{arxiv.2403.12026,
title = {FlexCap: Describe Anything in Images in Controllable Detail},
author = {Debidatta Dwibedi and Vidhi Jain and Jonathan Tompson and Andrew Zisserman and Yusuf Aytar},
journal= {arXiv preprint arXiv:2403.12026},
year = {2025}
}