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Cross-prompt inference boosts rare-class recall in imbalanced VLM finetuning

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员发现了一种称为“跨提示推理”的现象，该现象可以提高视觉语言模型（VLM）在不平衡数据集上训练时的性能。该方法包括在推理时使用与训练时不同的描述性提示，这可以恢复否则会被抑制的稀有类别的性能。该研究评估了六个数据集上的各种 VLM，发现该技术可持续提高稀有类别的召回率，尽管它可能以牺牲常见类别的召回率为代价。这种效应与训练-评估提示交互有关，并且在视觉混淆加剧稀有类别抑制时最强。 AI

影响 这项研究提供了一种技术，可以提高视觉语言模型在类别分布不平衡的数据集上的性能，从而可能带来更强大的 AI 系统。

排序理由 该集群包含一篇详细介绍改进模型性能的新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Cross-prompt inference boosts rare-class recall in imbalanced VLM finetuning

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该集群包含一篇详细介绍改进模型性能的新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Emanuel Ben Baruch, Sivan Biham, Alon Shoshan, Yochai Zvik, Avi Ben-Cohen ·

    交叉提示推理可减轻不平衡VLM微调中的稀有类别抑制

    arXiv:2610.21470v1 Announce Type: new Abstract: When a vision-language model is finetuned on class-imbalanced data, rare-class recall can fall well below the model's own zero-shot performance. In this regime, we uncover a surprising matched-prompt penalty whereby the standard cho…

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