Authors: Yunhao Ge, Xiaohui Zeng, Jacob Samuel Huffman, Tsung-Yi Lin, Ming-Yu Liu, Yin Cui

Published on: April 30, 2024

Impact Score: 7.6

Arxiv code: Arxiv:2404.19752

Summary

• What is new: The introduction of VisualFactChecker (VFC), a training-free pipeline that enhances detail and accuracy in captioning for 2D images and 3D objects.
• Why this is important: Existing captioning methods struggle with detail, content hallucination, and following instructions.
• What the research proposes: VFC uses a three-step pipeline including proposal, verification by large language models and specialized tools, and advanced caption generation.
• Results: VFC surpasses current open-sourced captioning methods on the COCO and Objaverse datasets in terms of image-text similarity, image-image similarity, and human evaluations.

Technical Details

Technological frameworks used: VFC Pipeline

Models used: Image-to-text captioning models, Large Language Models (LLM), Object Detection, VQA models, CLIP, GPT-4V

Data used: COCO dataset, Objaverse dataset

Potential Impact

Creative media, educational content production, accessibility technology companies, and firms requiring automated image and 3D object description.

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