Enhancing Model Performance: Another Approach to Vision-Language Instruction Tuning
This work addresses the challenge of improving vision-language instruction tuning for AI systems, representing an incremental advancement in multimodal model optimization.
The paper tackles the problem of enhancing multimodal functionalities in vision-language models by introducing Bottleneck Adapter, a novel approach that uses lightweight adapters for joint optimization, achieving 90.12% accuracy and outperforming human-level performance and LaVIN-7B.
The integration of large language models (LLMs) with vision-language (VL) tasks has been a transformative development in the realm of artificial intelligence, highlighting the potential of LLMs as a versatile general-purpose chatbot. However, the current trend in this evolution focuses on the integration of vision and language to create models that can operate in more diverse and real-world contexts. We present a novel approach, termed Bottleneck Adapter, specifically crafted for enhancing the multimodal functionalities of these complex models, enabling joint optimization of the entire multimodal LLM framework through a process known as Multimodal Model Tuning (MMT). Our approach utilizes lightweight adapters to connect the image encoder and LLM without the need for large, complex neural networks. Unlike the conventional modular training schemes, our approach adopts an end-to-end optimization regime, which, when combined with the adapters, facilitates the joint optimization using a significantly smaller parameter set. Our method exhibits robust performance with 90.12\% accuracy, outperforming both human-level performance (88.4\%) and LaVIN-7B (89.41\%).