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A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise

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arxiv 2312.12436 v2 pith:O6DMJXIC submitted 2023-12-19 cs.CV cs.AIcs.CLcs.MM

Chaoyou Fu , Renrui Zhang , Zihan Wang , Yubo Huang , Zhengye Zhang , Longtian Qiu , Gaoxiang Ye , Yunhang Shen
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Mengdan Zhang Peixian Chen Sirui Zhao Shaohui Lin Deqiang Jiang Di Yin Peng Gao Ke Li Hongsheng Li Xing Sun
This is my paper
classification cs.CVcs.AIcs.CLcs.MM
keywords geminigpt-4vvisualcapabilitiesmllmmulti-modalchallengerearly
verification ladder T0 review T1 audit T2 compute T3 formal
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The surge of interest towards Multi-modal Large Language Models (MLLMs), e.g., GPT-4V(ision) from OpenAI, has marked a significant trend in both academia and industry. They endow Large Language Models (LLMs) with powerful capabilities in visual understanding, enabling them to tackle diverse multi-modal tasks. Very recently, Google released Gemini, its newest and most capable MLLM built from the ground up for multi-modality. In light of the superior reasoning capabilities, can Gemini challenge GPT-4V's leading position in multi-modal learning? In this paper, we present a preliminary exploration of Gemini Pro's visual understanding proficiency, which comprehensively covers four domains: fundamental perception, advanced cognition, challenging vision tasks, and various expert capacities. We compare Gemini Pro with the state-of-the-art GPT-4V to evaluate its upper limits, along with the latest open-sourced MLLM, Sphinx, which reveals the gap between manual efforts and black-box systems. The qualitative samples indicate that, while GPT-4V and Gemini showcase different answering styles and preferences, they can exhibit comparable visual reasoning capabilities, and Sphinx still trails behind them concerning domain generalizability. Specifically, GPT-4V tends to elaborate detailed explanations and intermediate steps, and Gemini prefers to output a direct and concise answer. The quantitative evaluation on the popular MME benchmark also demonstrates the potential of Gemini to be a strong challenger to GPT-4V. Our early investigation of Gemini also observes some common issues of MLLMs, indicating that there still remains a considerable distance towards artificial general intelligence. Our project for tracking the progress of MLLM is released at https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models.

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Cited by 8 Pith papers

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    MME-RealWorld is the largest manually annotated high-resolution benchmark for MLLMs, where even the best models achieve less than 60% accuracy on challenging real-world tasks.

  2. MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?

    cs.CV 2024-03 conditional novelty 7.0 of 10

    MathVerse is a benchmark that tests multi-modal LLMs on visual math by providing each problem in six versions with progressively less diagram and text information to measure true visual understanding.

  3. Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

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    Video-LLaVA creates a unified visual representation for images and videos via pre-projection alignment, enabling mutual enhancement from joint training and strong results on image and video benchmarks.

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    InternLM-XComposer-2.5 is a 7B vision-language model supporting up to 96K context that reaches GPT-4V-level performance on image, video, and multi-turn tasks and adds LoRA-driven text-image composition capabilities.

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  8. A Survey on Multimodal Large Language Models

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