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English(EN) Test-Time Logit Prompting for Source-Free Missing Modality Adaptation

新的TLP框架在无源数据的情况下使视觉语言模型适应缺失的模态

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了测试时logit提示（TLP），一个新颖的框架，旨在提高视觉语言模型（VLMs）在测试期间缺失一种模态时的性能。与需要访问原始训练数据的先前方法不同，TLP以无源方式运行，使其适用于具有隐私或可访问性限制的应用。该框架使用不确定性感知调整和一致性正则化来优化logit提示，以保持语义完整性并适应预测置信度。实验表明，TLP只需很少的可调参数和几次优化步骤即可将识别性能提高多达8%。 AI

影响 使视觉语言模型在数据不完整的情况下能够在现实世界场景中更稳健地部署。

排序理由 该集群描述了一篇关于现有模型的新颖适应框架的详细研究论文。

在 Hugging Face Daily Papers 阅读 →

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新的TLP框架在无源数据的情况下使视觉语言模型适应缺失的模态

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报道来源 [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    面向无源缺失模态适应的测试时logit提示

    Vision-language models (VLMs) have achieved remarkable performance by leveraging complementary information from large-scale image-text pairs. However, missing-modality inputs are commonly encountered during real-world deployment, often leading to significant performance degradati…

  2. arXiv cs.CV TIER_1 English(EN) · Taixi Chen, Nancy Guo ·

    面向无源缺失模态适应的测试时logit提示

    arXiv:2609.02039v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved remarkable performance by leveraging complementary information from large-scale image-text pairs. However, missing-modality inputs are commonly encountered during real-world deployment, of…

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