ILVAD is a plug-and-play method that builds a saliency map from inter-layer attention discrepancies on early tokens to enhance visual evidence focus and ground generated text, reducing hallucinations in LVLMs.
LLaVA-NeXT: Improved reasoning, OCR, and world knowledge , url=
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VPiT enables pretrained LLMs to perform both visual understanding and generation by predicting discrete text tokens and continuous visual tokens, with understanding data proving more effective than generation-specific data.
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Finding the Correct Visual Evidence Without Forgetting: Mitigating Hallucination in LVLMs via Inter-Layer Visual Attention Discrepancy
ILVAD is a plug-and-play method that builds a saliency map from inter-layer attention discrepancies on early tokens to enhance visual evidence focus and ground generated text, reducing hallucinations in LVLMs.
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MetaMorph: Multimodal Understanding and Generation via Instruction Tuning
VPiT enables pretrained LLMs to perform both visual understanding and generation by predicting discrete text tokens and continuous visual tokens, with understanding data proving more effective than generation-specific data.