  Existing automatic captioning methods for visual content face challenges such
as lack of detail, content hallucination, and poor instruction following. In
this work, we propose VisualFactChecker (VFC), a flexible training-free
pipeline that generates high-fidelity and detailed captions for both 2D images
and 3D objects. VFC consists of three steps: 1) proposal, where image-to-text
captioning models propose multiple initial captions; 2) verification, where a
large language model (LLM) utilizes tools such as object detection and VQA
models to fact-check proposed captions; 3) captioning, where an LLM generates
the final caption by summarizing caption proposals and the fact check
verification results. In this step, VFC can flexibly generate captions in
various styles following complex instructions. We conduct comprehensive
captioning evaluations using four metrics: 1) CLIP-Score for image-text
similarity; 2) CLIP-Image-Score for measuring the image-image similarity
between the original and the reconstructed image generated by a text-to-image
model using the caption. 3) human study on Amazon Mechanical Turk; 4) GPT-4V
for fine-grained evaluation. Evaluation results show that VFC outperforms
state-of-the-art open-sourced captioning methods for 2D images on the COCO
dataset and 3D assets on the Objaverse dataset. Our study demonstrates that by
combining open-source models into a pipeline, we can attain captioning
capability comparable to proprietary models such as GPT-4V, despite being over
10x smaller in model size.
