PaLM2-VAdapter: Progressively Aligned Language Model Makes a Strong Vision-language Adapter
(2402.10896)Abstract
This paper demonstrates that a progressively aligned language model 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 language model 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-language models, marking a significant efficiency improvement.
Overview
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PaLM2-VAdapter introduces a progressive alignment strategy to efficiently integrate LLMs and vision encoders, enhancing vision-language model performance.
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The methodology bypasses the need for developing new models by using robust unimodal models for constructing sophisticated Large Vision-Language Models (LVLMs).
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It employs a two-stage progressive training strategy, significantly improving model convergence, performance, and scalability across various multimodal tasks.
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The model has demonstrated superior efficiency, requiring 30-70% fewer parameters than current state-of-the-art LVLMs, and offers potential applications in augmented reality and interactive AI systems.
PaLM2-VAdapter Enhances Vision-Language Model Efficiency and Efficacy
Introduction
The realm of Large Vision-Language Models (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 Language Model 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 language models 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 language model 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 language model 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 language models, 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.
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