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GPT4Vis: What Can GPT-4 Do for Zero-shot Visual Recognition?
GPT4Vis: What Can GPT-4 Do for Zero-shot Visual Recognition?
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Length:
40 minutes
Released:
Dec 5, 2023
Format:
Podcast episode
Description
This paper does not present a novel method. Instead, it delves into an essential, yet must-know baseline in light of the latest advancements in Generative Artificial Intelligence (GenAI): the utilization of GPT-4 for visual understanding. Our study centers on the evaluation of GPT-4's linguistic and visual capabilities in zero-shot visual recognition tasks. Specifically, we explore the potential of its generated rich textual descriptions across various categories to enhance recognition performance without any training. Additionally, we evaluate its visual proficiency in directly recognizing diverse visual content. To achieve this, we conduct an extensive series of experiments, systematically quantifying the performance of GPT-4 across three modalities: images, videos, and point clouds. This comprehensive evaluation encompasses a total of 16 widely recognized benchmark datasets, providing top-1 and top-5 accuracy metrics. Our study reveals that leveraging GPT-4's advanced linguistic knowledge to generate rich descriptions markedly improves zero-shot recognition. In terms of visual proficiency, GPT-4V's average performance across 16 datasets sits roughly between the capabilities of OpenAI-CLIP's ViT-L and EVA-CLIP's ViT-E. We hope that this research will contribute valuable data points and experience for future studies. We release our code at https://github.com/whwu95/GPT4Vis.
2023: Wenhao Wu, Huanjin Yao, Mengxi Zhang, Yuxin Song, Wanli Ouyang, Jingdong Wang
https://arxiv.org/pdf/2311.15732v1.pdf
2023: Wenhao Wu, Huanjin Yao, Mengxi Zhang, Yuxin Song, Wanli Ouyang, Jingdong Wang
https://arxiv.org/pdf/2311.15732v1.pdf
Released:
Dec 5, 2023
Format:
Podcast episode
Titles in the series (100)
Simple synthetic data reduces sycophancy in large language models: Sycophancy is an undesirable behavior where models tailor their responses to follow a human user's view even when that view is not objectively correct (e.g., adapting liberal views once a user reveals that they are liberal). In this paper, we study t... by Papers Read on AI