Can MLLMs Understand the Deep Implication Behind Chinese Images?

• URL: http://arxiv.org/abs/2410.13854v1
• Date: Thu, 17 Oct 2024 17:59:24 GMT
• Title: Can MLLMs Understand the Deep Implication Behind Chinese Images?
• Authors: Chenhao Zhang, Xi Feng, Yuelin Bai, Xinrun Du, Jinchang Hou, Kaixin Deng, Guangzeng Han, Qinrui Li, Bingli Wang, Jiaheng Liu, Xingwei Qu, Yifei Zhang, Qixuan Zhao, Yiming Liang, Ziqiang Liu, Feiteng Fang, Min Yang, Wenhao Huang, Chenghua Lin, Ge Zhang, Shiwen Ni,
• Abstract summary: We introduce the **C**hinese **I**mage **I**mplication understanding **Bench**mark, **CII-Bench**, which aims to assess the higher-order perception and understanding capabilities of MLLMs for Chinese images. Images in CII-Bench are sourced from the Chinese Internet and manually reviewed, with corresponding answers also manually crafted. CII-Bench incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, which can deeply reflect the model's understanding of Chinese traditional culture.
• Score: 29.007010549079098
• License:
• Abstract: As the capabilities of Multimodal Large Language Models (MLLMs) continue to improve, the need for higher-order capability evaluation of MLLMs is increasing. However, there is a lack of work evaluating MLLM for higher-order perception and understanding of Chinese visual content. To fill the gap, we introduce the **C**hinese **I**mage **I**mplication understanding **Bench**mark, **CII-Bench**, which aims to assess the higher-order perception and understanding capabilities of MLLMs for Chinese images. CII-Bench stands out in several ways compared to existing benchmarks. Firstly, to ensure the authenticity of the Chinese context, images in CII-Bench are sourced from the Chinese Internet and manually reviewed, with corresponding answers also manually crafted. Additionally, CII-Bench incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, which can deeply reflect the model's understanding of Chinese traditional culture. Through extensive experiments on CII-Bench across multiple MLLMs, we have made significant findings. Initially, a substantial gap is observed between the performance of MLLMs and humans on CII-Bench. The highest accuracy of MLLMs attains 64.4%, where as human accuracy averages 78.2%, peaking at an impressive 81.0%. Subsequently, MLLMs perform worse on Chinese traditional culture images, suggesting limitations in their ability to understand high-level semantics and lack a deep knowledge base of Chinese traditional culture. Finally, it is observed that most models exhibit enhanced accuracy when image emotion hints are incorporated into the prompts. We believe that CII-Bench will enable MLLMs to gain a better understanding of Chinese semantics and Chinese-specific images, advancing the journey towards expert artificial general intelligence (AGI). Our project is publicly available at https://cii-bench.github.io/.

Related papers

• MC-Bench: A Benchmark for Multi-Context Visual Grounding in the Era of MLLMs [61.56904387052982]
  This paper proposes a new visual grounding task called multi-context visual grounding. It aims to localize instances of interest across multiple images based on open-ended text prompts. We benchmark over 20 state-of-the-art MLLMs and foundation models with potential multi-context visual grounding capabilities.
  arXiv  Detail & Related papers  (2024-10-16T07:52:57Z)
• Benchmarking Chinese Knowledge Rectification in Large Language Models [43.9841600678381]
  This paper introduces a benchmark for rectifying Chinese knowledge in Large Language Models via knowledge editing. We collect seven type of knowledge from various sources, including classical texts, idioms, and content from Baidu Tieba Ruozhiba. Through the analysis of this dataset, we uncover the challenges faced by current LLMs in mastering Chinese.
  arXiv  Detail & Related papers  (2024-09-09T17:11:51Z)
• II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models [49.070801221350486]
  multimodal large language models (MLLMs) have consistently led to new breakthroughs on various benchmarks. We propose the Image Implication understanding Benchmark, II-Bench, which aims to evaluate the model's higher-order perception of images.
  arXiv  Detail & Related papers  (2024-06-09T17:25:47Z)
• The Instinctive Bias: Spurious Images lead to Illusion in MLLMs [34.91795817316696]
  We identify a typical class of inputs that baffles MLLMs, which consist of images that are highly relevant but inconsistent with answers. We propose CorrelationQA, the first benchmark that assesses the visual illusion level given spurious images. We conduct a thorough analysis on 9 mainstream MLLMs, illustrating that they universally suffer from this instinctive bias to varying degrees.
  arXiv  Detail & Related papers  (2024-02-06T06:48:46Z)
• AesBench: An Expert Benchmark for Multimodal Large Language Models on Image Aesthetics Perception [64.25808552299905]
  AesBench is an expert benchmark aiming to comprehensively evaluate the aesthetic perception capacities of MLLMs. We construct an Expert-labeled Aesthetics Perception Database (EAPD), which features diversified image contents and high-quality annotations provided by professional aesthetic experts. We propose a set of integrative criteria to measure the aesthetic perception abilities of MLLMs from four perspectives, including Perception (AesP), Empathy (AesE), Assessment (AesA) and Interpretation (AesI)
  arXiv  Detail & Related papers  (2024-01-16T10:58:07Z)
• MM-SAP: A Comprehensive Benchmark for Assessing Self-Awareness of Multimodal Large Language Models in Perception [21.60103376506254]
  Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in visual perception and understanding. These models also suffer from hallucinations, which limit their reliability as AI systems. This paper aims to define and evaluate the self-awareness of MLLMs in perception.
  arXiv  Detail & Related papers  (2024-01-15T08:19:22Z)
• LION : Empowering Multimodal Large Language Model with Dual-Level Visual Knowledge [58.82222646803248]
  Multimodal Large Language Models (MLLMs) have endowed LLMs with the ability to perceive and understand multi-modal signals. Most of the existing MLLMs mainly adopt vision encoders pretrained on coarsely aligned image-text pairs, leading to insufficient extraction and reasoning of visual knowledge. We propose a dual-Level vIsual knedgeOwl eNhanced Multimodal Large Language Model (LION), which empowers the MLLM by injecting visual knowledge in two levels.
  arXiv  Detail & Related papers  (2023-11-20T15:56:44Z)
• Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision [85.6008224440157]
  Multi-modality Large Language Models (MLLMs) have catalyzed a shift in computer vision from specialized models to general-purpose foundation models. We present Q-Bench, a holistic benchmark crafted to evaluate potential abilities of MLLMs on three realms: low-level visual perception, low-level visual description, and overall visual quality assessment.
  arXiv  Detail & Related papers  (2023-09-25T14:43:43Z)
• CMMLU: Measuring massive multitask language understanding in Chinese [133.70911295934746]
  This paper introduces a comprehensive Chinese benchmark that covers various subjects, including natural science, social sciences, engineering, and humanities. CMMLU fills the gap in evaluating the knowledge and reasoning capabilities of large language models within the Chinese context.
  arXiv  Detail & Related papers  (2023-06-15T15:49:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.