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

Kaizhi Zheng, Xuehai He, Xin Eric Wang

Keywords: Multimodal Generation; Multimodal Large Language Model

TL;DR: We propose a unified strucuture for both multimodal comprehension and generation

Abstract: Large Language Models (LLMs) have garnered significant attention for their advancements in natural language processing, demonstrating unparalleled prowess in text comprehension and generation. Yet, the simultaneous generation of images with coherent textual narratives remains an evolving frontier. In response, we introduce an innovative interleaved vision-and-language generation technique anchored by the concept of ``generative vokens," acting as the bridge for harmonized image-text outputs. Our approach is characterized by a distinctive two-staged training strategy focusing on description-free multimodal generation, where the training requires no comprehensive descriptions of images. To bolster model integrity, classifier-free guidance is incorporated, enhancing the effectiveness of vokens on image generation. Our model, MiniGPT-5, exhibits substantial improvement over the baseline Divter model on the MMDialog dataset and consistently delivers superior or comparable multimodal outputs in human evaluations on the VIST dataset, highlighting its efficacy across diverse benchmarks.

Primary Area: representation learning for computer vision, audio, language, and other modalities

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Submission Number: 2099