PND reduces object hallucination in VLMs via a dual-path contrast during decoding that amplifies visual features and penalizes linguistic priors, achieving reported SOTA results on POPE, MME, and CHAIR without retraining.
A survey on multimodal large language models.National Science Review, 11(12): nwae403
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A 0.5B student VLM distills from a 3B teacher using visual-switch distillation and DBiLD loss to gain 3.6 points on average across 10 multimodal benchmarks without architecture changes.
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Breaking the Illusion: When Positive Meets Negative in Multimodal Decoding
PND reduces object hallucination in VLMs via a dual-path contrast during decoding that amplifies visual features and penalizes linguistic priors, achieving reported SOTA results on POPE, MME, and CHAIR without retraining.
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Switch-KD: Visual-Switch Knowledge Distillation for Vision-Language Models
A 0.5B student VLM distills from a 3B teacher using visual-switch distillation and DBiLD loss to gain 3.6 points on average across 10 multimodal benchmarks without architecture changes.