How does GPT-4o's performance on ARC-Challenge compare to other state-of-the-art multimodal models like Flamin

Abstract

Abstract: Getting a good feature representation of data is paramount for Human Activity Recognition (HAR) using wearable sensors. An increasing number of feature learning approaches-in particular deep-learning based-have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to a

Research Question

How does GPT-4o's performance on ARC-Challenge compare to other state-of-the-art multimodal models like Flamingo or Gemini in few-shot learning scenarios?

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