Introduces VIG metric to measure visual contribution via perplexity reduction and applies it for selective training of LVLMs on high-VIG samples and tokens to improve grounding with reduced supervision.
Looking beyond text: Reducing language bias in large vision-language models via multimodal dual-attention and soft-image guidance
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Focusing Where Vision Matters: Selective Training for Large Vision Language Models via Visual Information Gain
Introduces VIG metric to measure visual contribution via perplexity reduction and applies it for selective training of LVLMs on high-VIG samples and tokens to improve grounding with reduced supervision.