MOVE: A Mixture-of-Vision-Encoders Approach for Domain-Focused Vision-Language Processing

• URL: http://arxiv.org/abs/2502.15381v1
• Date: Fri, 21 Feb 2025 11:05:30 GMT
• Title: MOVE: A Mixture-of-Vision-Encoders Approach for Domain-Focused Vision-Language Processing
• Authors: Matvey Skripkin, Elizaveta Goncharova, Dmitrii Tarasov, Andrey Kuznetsov,
• Abstract summary: Multimodal language models (MLMs) integrate visual and textual information by coupling a vision encoder with a large language model through the specific adapter.<n>We propose MOVE (Mixture of Visions) to leverage multiple pre-trained encoders for specialized tasks.
• Score: 2.0249250133493195
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal language models (MLMs) integrate visual and textual information by coupling a vision encoder with a large language model through the specific adapter. While existing approaches commonly rely on a single pre-trained vision encoder, there is a great variability of specialized encoders that can boost model's performance in distinct domains. In this work, we propose MOVE (Mixture of Vision Encoders) a simple yet effective approach to leverage multiple pre-trained encoders for specialized multimodal tasks. MOVE automatically routes inputs to the most appropriate encoder among candidates such as Unichat, InternViT, and Texify, thereby enhancing performance across a diverse set of benchmarks, including ChartQA, MMBench, and MMMU. Experimental results demonstrate that MOVE achieves competitive accuracy without incurring the complexities of image slicing for high-resolution images.

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