Summary of G-veval: a Versatile Metric For Evaluating Image and Video Captions Using Gpt-4o, by Tony Cheng Tong et al.
G-VEval: A Versatile Metric for Evaluating Image and Video Captions Using GPT-4o
by Tony Cheng Tong, Sirui He, Zhiwen Shao, Dit-Yan Yeung
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel evaluation metric, G-VEval, for visual captioning tasks. The traditional metrics like BLEU, METEOR, CIDEr, and ROUGE often miss semantic depth, while trained metrics such as CLIP-Score, PAC-S, and Polos are limited in zero-shot scenarios. Advanced Language Model-based metrics also struggle with aligning to nuanced human preferences. To address these issues, the authors introduce G-VEval, a novel metric inspired by G-Eval and powered by the new GPT-4o. The proposed metric uses chain-of-thought reasoning in large multimodal models and supports three modes: reference-free, reference-only, and combined, accommodating both video and image inputs. Additionally, the paper proposes MSVD-Eval, a new dataset for video captioning evaluation, to establish a more transparent and consistent framework for both human experts and evaluation metrics. G-VEval outperforms existing methods in correlation with human annotations, as measured by Kendall tau-b and Kendall tau-c. This provides a flexible solution for diverse captioning tasks and suggests a straightforward yet effective approach for large language models to understand video content, paving the way for advancements in automated captioning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new evaluation metric, G-VEval, which aims to address issues with current metrics used for visual captioning. The authors explain that traditional metrics often miss semantic depth and are limited in zero-shot scenarios. They propose G-VEval as a novel metric inspired by G-Eval, powered by the new GPT-4o model. This metric uses chain-of-thought reasoning in large multimodal models and supports three modes: reference-free, reference-only, and combined, for both video and image inputs. The paper also proposes MSVD-Eval, a new dataset for video captioning evaluation, to establish a more transparent framework for both human experts and evaluation metrics. The authors demonstrate that G-VEval outperforms existing methods in correlation with human annotations, providing a flexible solution for diverse captioning tasks. |
Keywords
» Artificial intelligence » Bleu » Gpt » Language model » Rouge » Zero shot
