MiniGPT-5: Interleaved Vision-and-Language Generation via Generative Vokens

• URL: http://arxiv.org/abs/2310.02239v3
• Date: Fri, 15 Mar 2024 21:54:08 GMT
• Title: MiniGPT-5: Interleaved Vision-and-Language Generation via Generative Vokens
• Authors: Kaizhi Zheng, Xuehai He, Xin Eric Wang,
• Abstract summary: We introduce a novel interleaved vision-and-language generation method, centered around the concept of generative vokens. Our method is marked by a unique two-stage training strategy for description-free multimodal generation. Our model, MiniGPT-5, exhibits substantial improvement over the baseline models on multimodal generation datasets.
• Score: 22.802963850131306
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The effectiveness of Multimodal Large Language Models (MLLMs) demonstrates a profound capability in multimodal understanding. However, the simultaneous generation of images with coherent texts is still underdeveloped. Addressing this, we introduce a novel interleaved vision-and-language generation method, centered around the concept of ``generative vokens". These vokens serve as pivotal elements contributing to coherent image-text outputs. Our method is marked by a unique two-stage training strategy for description-free multimodal generation, which does not necessitate extensive descriptions of images. We integrate classifier-free guidance to enhance the alignment of generated images and texts, ensuring more seamless and contextually relevant multimodal interactions. Our model, MiniGPT-5, exhibits substantial improvement over the baseline models on multimodal generation datasets, including MMDialog and VIST. The human evaluation shows MiniGPT-5 is better than the baseline model on more than 56\% cases for multimodal generation, highlighting its efficacy across diverse benchmarks.

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