Summary of Ii-bench: An Image Implication Understanding Benchmark For Multimodal Large Language Models, by Ziqiang Liu et al.

II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models

by Ziqiang Liu, Feiteng Fang, Xi Feng, Xinrun Du, Chenhao Zhang, Zekun Wang, Yuelin Bai, Qixuan Zhao, Liyang Fan, Chengguang Gan, Hongquan Lin, Jiaming Li, Yuansheng Ni, Haihong Wu, Yaswanth Narsupalli, Zhigang Zheng, Chengming Li, Xiping Hu, Ruifeng Xu, Xiaojun Chen, Min Yang, Jiaheng Liu, Ruibo Liu, Wenhao Huang, Ge Zhang, Shiwen Ni

First submitted to arxiv on: 9 Jun 2024

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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 rapid advancements in multimodal large language models (MLLMs) have led to new breakthroughs on various benchmarks. However, there is a lack of exploration into the higher-order perceptual capabilities of MLLMs. To address this gap, the Image Implication understanding Benchmark (II-Bench) evaluates the model’s higher-order perception of images. Through experiments, significant findings are made. Initially, a performance gap is observed between MLLMs and humans on II-Bench. The pinnacle accuracy of MLLMs reaches 74.8%, while human accuracy averages 90% with a peak of 98%. Subsequently, MLLMs perform worse on abstract and complex images, suggesting limitations in their ability to understand high-level semantics and capture image details. Incorporating image sentiment polarity hints into prompts also enhances model accuracy, underscoring a deficiency in their inherent understanding of image sentiment.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper proposes the Image Implication understanding Benchmark (II-Bench) to evaluate the higher-order perception capabilities of multimodal large language models (MLLMs). The benchmark aims to bridge the gap between MLLMs and humans’ abilities. The paper presents findings from extensive experiments on II-Bench across multiple MLLMs, revealing a performance gap between machines and humans. MLLMs struggle with understanding abstract and complex images, suggesting limitations in their ability to capture high-level semantics. Additionally, incorporating image sentiment polarity hints improves model accuracy.