Multimodal ICL lags text-only ICL in few-shot settings due to weak cross-modal reasoning alignment and unreliable task mapping transfer, with an inference-stage method proposed to strengthen transfer.
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Online In-Context Distillation lets small VLMs gain up to 33% performance with as little as 4% teacher annotations by distilling knowledge through dynamic in-context demonstrations at inference.
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Why Multimodal In-Context Learning Lags Behind? Unveiling the Inner Mechanisms and Bottlenecks
Multimodal ICL lags text-only ICL in few-shot settings due to weak cross-modal reasoning alignment and unreliable task mapping transfer, with an inference-stage method proposed to strengthen transfer.
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Online In-Context Distillation for Low-Resource Vision Language Models
Online In-Context Distillation lets small VLMs gain up to 33% performance with as little as 4% teacher annotations by distilling knowledge through dynamic in-context demonstrations at inference.