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.
Making the v in vqa matter: Elevating the role of image understanding in visual question answering
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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.