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    <abstract>Understanding the deep semantics of images is essential in the era dominated by social media. However, current research works primarily on the superficial description of images, revealing a notable deficiency in the systematic investigation of the inherent deep semantics. In this work, we introduce DEEPEVAL, a comprehensive benchmark to assess Large Multimodal Models’ (LMMs) capacities of visual deep semantics. DEEPEVAL includes human-annotated dataset and three progressive subtasks: fine-grained description selection, in-depth title matching, and deep semantics understanding. Utilizing DEEPEVAL, we evaluate 9 open-source LMMs and GPT-4V(ision). Our evaluation demonstrates a substantial gap between the deep semantic comprehension capabilities of existing LMMs and humans. For example, GPT-4V is 30% behind humans in understanding deep semantics, even though it achieves human-comparable performance in image description. Further analysis reveals that LMM performance on DEEPEVAL varies according to the specific facets of deep semantics explored, indicating the fundamental challenges remaining in developing LMMs.</abstract>
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%0 Conference Proceedings
%T Can Large Multimodal Models Uncover Deep Semantics Behind Images?
%A Yang, Yixin
%A Li, Zheng
%A Dong, Qingxiu
%A Xia, Heming
%A Sui, Zhifang
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F yang-etal-2024-large
%X Understanding the deep semantics of images is essential in the era dominated by social media. However, current research works primarily on the superficial description of images, revealing a notable deficiency in the systematic investigation of the inherent deep semantics. In this work, we introduce DEEPEVAL, a comprehensive benchmark to assess Large Multimodal Models’ (LMMs) capacities of visual deep semantics. DEEPEVAL includes human-annotated dataset and three progressive subtasks: fine-grained description selection, in-depth title matching, and deep semantics understanding. Utilizing DEEPEVAL, we evaluate 9 open-source LMMs and GPT-4V(ision). Our evaluation demonstrates a substantial gap between the deep semantic comprehension capabilities of existing LMMs and humans. For example, GPT-4V is 30% behind humans in understanding deep semantics, even though it achieves human-comparable performance in image description. Further analysis reveals that LMM performance on DEEPEVAL varies according to the specific facets of deep semantics explored, indicating the fundamental challenges remaining in developing LMMs.
%U https://aclanthology.org/2024.findings-acl.113
%P 1898-1912

Markdown (Informal)

[Can Large Multimodal Models Uncover Deep Semantics Behind Images?](https://aclanthology.org/2024.findings-acl.113) (Yang et al., Findings 2024)

• Can Large Multimodal Models Uncover Deep Semantics Behind Images? (Yang et al., Findings 2024)

ACL

• Yixin Yang, Zheng Li, Qingxiu Dong, Heming Xia, and Zhifang Sui. 2024. Can Large Multimodal Models Uncover Deep Semantics Behind Images?. In Findings of the Association for Computational Linguistics ACL 2024, pages 1898–1912, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.