Large vision-language models (VLMs) face challenges with visual components and long tokens, limiting their ability to interpret complex information. A new approach proposes using ensemble techniques to combine strengths of visual encoders and language models. Testing with six experts showed enhanced performance, especially with triple experts. This method can improve VLMs’ ability to handle complex information.
Overcoming Challenges in Vision-Language Models (VLMs)
Introduction
Large vision-language models (VLMs) face challenges in accurately interpreting complex visual information and contextual details. To address these limitations, a novel approach has been introduced to leverage ensemble expert techniques and enhance the performance and versatility of VLMs.
Proposed Solution
The solution involves synergizing the strengths of individual visual encoders, such as image-text matching, OCR, and image segmentation, through a fusion network. This harmonizes the processing of outputs from diverse visual experts, bridging the gap between image encoders and pre-trained language models (LLMs).
Effectiveness of Poly-Visual Experts
The approach adopts a poly-visual-expert perspective, similar to the vertebrate visual system, to address concerns regarding the effectiveness, integration, and length limitations of multiple visual experts in VLMs. Experimental results demonstrate that an increasing number of visual experts leads to an overall improvement in multimodal capability across various benchmarks.
Performance Boost
Experimental results consistently show the superior performance of VLMs employing multiple experts compared to isolated visual encoders. The integration of additional experts significantly enhances the capabilities of vision-language models, surpassing the accuracy and depth of understanding achieved by existing models.
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