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About this paper
Xin He, Longhui Wei, Lingxi Xie, Qi Tian
Researchers at Huawei Inc. developed the Incorporating Visual Experts (IVE) framework to address information loss in Multimodal Large Language Models (MLLMs). This framework integrates diverse visual encoders and explicit structural knowledge throughout both training and inference, leading to improved performance across various visual question answering tasks.
Key insights
- Integrating structural knowledge during both training and inference is crucial for robust performance; merely using noisy expert data during inference can degrade results.
- Training the MLLM to interpret and leverage potentially noisy, automatically extracted structural information (like object coordinates and OCR text) is essential for effectively enhancing visual understanding.
- The combination of specialized multi-task encoders (semantic, low-level, document-related) provides complementary visual information, leading to improved understanding across diverse tasks and boosting spatial awareness.
Summary by alphaXiv.
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