Visual Fact Checker: Enabling High-Fidelity Detailed Caption Generation

TL;DR

This paper introduces VisualFactChecker, a training-free pipeline that generates detailed, high-fidelity captions for images and 3D objects by combining multiple models and fact-checking, outperforming existing methods.

Contribution

The paper presents a novel, flexible, training-free captioning pipeline that integrates multiple models and fact-checking to improve caption detail and fidelity.

Findings

VFC outperforms state-of-the-art captioning methods on COCO and Objaverse datasets.

VFC achieves comparable captioning quality to GPT-4V with significantly smaller models.

Comprehensive evaluations demonstrate VFC's effectiveness across multiple metrics.

Abstract

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.