Double-Softmax Prompt Tuning uses sequential softmax normalization to create self-adaptive gradient saturation that filters noisy samples while preserving useful updates in CLIP prompt tuning.
Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , booktitle =
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MPD reduces hallucinations in LVLMs by 23.4% while retaining 97.4% of general capability through semantic disentanglement and selective parameter updates.
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Intrinsic Gradient Suppression for Label-Noise Prompt Tuning in Vision-Language Models
Double-Softmax Prompt Tuning uses sequential softmax normalization to create self-adaptive gradient saturation that filters noisy samples while preserving useful updates in CLIP prompt tuning.
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Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation
MPD reduces hallucinations in LVLMs by 23.4% while retaining 97.4% of general capability through semantic disentanglement and selective parameter updates.