PaLM2-VAdapter: Progressively Aligned Language Model Makes a Strong Vision-language Adapter (2402.10896v2)

Abstract: This paper demonstrates that a progressively aligned LLM can effectively bridge frozen vision encoders and LLMs. While the fundamental architecture and pre-training methods of vision encoders and LLMs have been extensively studied, the architecture and training strategy of vision-language adapters vary significantly across recent works. Our research undertakes a thorough exploration of the state-of-the-art perceiver resampler architecture and builds a strong baseline. However, we observe that the vision-language alignment with perceiver resampler exhibits slow convergence and limited scalability with a lack of direct supervision. To address this issue, we propose PaLM2-VAdapter, employing a progressively aligned LLM as the vision-language adapter. Compared to the strong baseline with perceiver resampler, our method empirically shows faster convergence, higher performance, and stronger scalability. Extensive experiments across various Visual Question Answering (VQA) and captioning tasks on both images and videos demonstrate that our model exhibits state-of-the-art visual understanding and multi-modal reasoning capabilities. Notably, our method achieves these advancements with 30~70% fewer parameters than the state-of-the-art large vision-LLMs, marking a significant efficiency improvement.

• The paper introduces a progressive training strategy that repurposes a small PaLM2 model as a vision-language adapter to enable faster convergence and superior performance.
• It leverages pre-trained vision encoders and language models to construct efficient LVLMs using 30-70% fewer parameters than conventional approaches.
• Empirical results show that PaLM2-VAdapter outperforms state-of-the-art models in visual captioning and QA tasks while offering enhanced scalability.

PaLM2-VAdapter Enhances Vision-LLM Efficiency and Efficacy

Introduction

The field of Large Vision-LLMs (LVLMs) has witnessed significant advancements, with a notable shift towards leveraging pre-trained and frozen vision encoders and LLMs to foster cross-modal understanding and alignment. The paper on "PaLM2-VAdapter: Progressively Aligned LLM Makes a Strong Vision-language Adapter" embodies this contemporary approach by introducing a progressive alignment strategy aimed at efficiently and effectively bridging these pre-trained models without necessitating extensive re-training.

The Core of PaLM2-VAdapter

The PaLM2-VAdapter methodology circumvents the need for developing novel vision and LLMs from scratch by integrating robust unimodal models — vision encoders and LLMs. This integration facilitates the construction of sophisticated LVLMs capable of exhibiting remarkable performance across various multimodal benchmarks.

Vision-Language Alignment

The paper embarks on a meticulous exploration of the current architectures used for vision-language adapters, specifically focusing on the state-of-the-art perceiver resampler architecture, and establishes a strong baseline. Despite the evident prowess of this configuration, challenges in terms of slow convergence and scalability surfaced. To address these issues, the researchers propose the innovative PaLM2-VAdapter, which harnesses a progressively aligned LLM to act as a vision-language adapter, demonstrating faster convergence, elevated performance, and stronger scalability.

Progressive Training Strategy

Unique to PaLM2-VAdapter is the progressive training strategy, where a tiny PaLM-2 model is first employed as a LLM decoder and subsequently re-trained as the adapter for consolidating the vision encoder and a significantly larger PaLM-2 model. This two-stage training approach not only ensures rapid model convergence but also enhances the model's performance and scalability.

Empirical Findings and Benchmarks

The PaLM2-VAdapter was subjected to extensive experiments across various visual captioning and Question Answering (QA) tasks involving images and videos. The model consistently outshone state-of-the-art LVLMs while requiring 30-70% fewer parameters — a testament to its superior efficiency. In comparison to baseline models employing perceiver resampler adapters, the PaLM2-VAdapter showcased significantly accelerated convergence rates, superior performance metrics, and enhanced scalability.

Implications and Potential Future Directions

The introduction of PaLM2-VAdapter has several theoretical and practical implications. From a theoretical perspective, it underscores the potential of progressive alignment strategies in optimizing the interaction between pre-trained unimodal models for multimodal tasks. Practically, this work provides a blueprint for constructing high-performing, efficient LVLMs that can be further fine-tuned for diverse applications beyond visual captioning and QA, such as augmented reality and interactive AI systems. Future research could explore the application of similar strategies to other modalities or investigate the integration of additional linguistic or visual nuance understanding to further push the boundaries of what these models can achieve.

Conclusion

The PaLM2-VAdapter represents a significant stride in the consolidation of vision and LLMs, setting new standards for efficiency, performance, and scalability in the LVLM domain. Its progressive training strategy not only mitigates the training complexities associated with large multimodal models but also paves the way for advanced LVLMs capable of even more nuanced understanding and interaction with the visual and linguistic world.