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About this paper
Xiangyu Zhao, Xiangtai Li, Haodong Duan, Haian Huang, Yining Li, Kai Chen, Hua Yang
MG-LLaVA enhances Multimodal Large Language Models (MLLMs) by systematically integrating low-resolution global context, high-resolution fine details, and explicit object-level features, achieving state-of-the-art performance across various perception and visual question answering benchmarks.
Key insights
- Systematically combining global context, fine-grained details, and explicit object-level features significantly enhances an MLLM's perceptual capabilities across diverse tasks.
- The Conv-Gate fusion mechanism provides an efficient way to integrate heterogeneous visual features from different resolutions and architectural backbones without a prohibitive increase in computational cost.
- Direct concatenation of object-level features with multi-resolution features as input to the Large Language Model (LLM) is more effective than cross-attention for allowing the LLM to leverage multi-granularity information.
Summary by alphaXiv.
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