MiniGPT-5: Interleaved Vision-and-Language Generation via Generative Vokens (2310.02239v3)

Abstract: The effectiveness of Multimodal LLMs (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.

• The paper introduces generative vokens to bridge visual and textual features, markedly enhancing multimodal output alignment.
• It employs a two-stage training strategy that supports description-free generation and efficient fine-tuning with minimal annotated data.
• Empirical evaluations demonstrate over 56% improvement in multimodal generation quality, with superior performance on CLIP, S-BERT, and FID metrics.

Interleaved Vision-and-Language Generation: An Evaluation of MiniGPT-5

The paper "MiniGPT-5: Interleaved Vision-and-Language Generation via Generative Vokens" introduces a novel approach addressing the challenges associated with generating coherent multimodal outputs from LLMs. While LLMs have demonstrated significant proficiency in text comprehension and generation, the seamless integration of vision and language generation remains a convoluted task. The research leverages the concept of "generative vokens", a term denoting visual tokens that bridge textual and visual spaces, contributing significantly to the alignment of text and image generation without the necessity for extensive image descriptions.

Core Contributions

1. Generative Vokens: The authors propose an innovative framework utilizing generative vokens, which facilitate the transition between textual and visual features. By integrating generative vokens with LLMs and Stable Diffusion, their approach aims to overcome limitations in existing vision-and-language generation models. The introduction of generative vokens aids in producing contextually aligned and coherent outputs, addressing the gap between vision and text feature spaces.
2. Two-Stage Training Strategy: The research adopts a two-stage training methodology, focusing initially on description-free multimodal generation followed by parameter-efficient fine-tuning. This strategic alignment allows the model to adapt efficiently to multimodal tasks without requiring copious amounts of annotated data. The dual-loss functionality and classifier-free guidance reinforce the alignment between vision and language modalities, thereby improving generation quality.
3. Empirical Performance: Through comprehensive evaluations, MiniGPT-5 demonstrates considerable advancements over baseline models across several multimodal datasets, including MMDialog and VIST. Notably, MiniGPT-5 shows enhanced multimodal generation capabilities in over 56% of the evaluated cases when compared to baselines, underscoring its efficacy and robustness in generating contextually appropriate outputs.

Experimental Insights

• Image Generation Metrics: MiniGPT-5 outperforms several existing models in generating high-quality images that maintain semantic coherence, as assessed by CLIP-based metrics and FID scores. This suggests that the model effectively leverages the generative vokens to enhance visual generation.
• Textual Cohesion: The model is benchmarked against state-of-the-art systems in multimodal datasets, reporting higher textual continuity and coherence scores, such as S-BERT and Rouge-L metrics. These results indicate the model's ability to produce text that aligns well with preceding contextual inputs.
• Human Evaluations: The paper presents human evaluations, highlighting that MiniGPT-5 surpasses other methods in providing more appropriate, coherent multimodal outputs. Human assessments focused on language continuity, image quality, and multimodal coherence consistently favored MiniGPT-5 over other competitive models like GILL and two-stage baselines.

Implications and Future Work

MiniGPT-5's approach to interleaved vision-and-language generation opens new avenues for applications in automated dialogue systems, multimedia content creation, and beyond. The paper paves the way for further explorations into multimodal LLMs where the focus shifts from merely enhancing comprehension to fostering more naturalistic and contextually relevant interactions across multiple modalities.

Future studies could explore augmenting the generative capabilities of these models, investigating methods to fine-tune LLMs for even greater memory and computational efficiency. Additionally, while the current model significantly improves multimodal generation, there remains potential in enhancing the fidelity of object textures within generated images.

This research exemplifies the progression towards bridging text and visual generation, emphasizing the need for adaptable and robust multimodal frameworks in advancing AI technologies.