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English(EN) What Makes Synthetic Hard Negatives Work in Vision-Language Pretraining?

新的SNAP方法通过合成负例增强视觉-语言预训练

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为SNAP的新方法，通过生成有效的合成难负例来改进视觉-语言预训练。现有方法在跨模态构建（会产生过于容易的负例）或内模态构建（包含正例）方面存在困难。SNAP通过创建避免任一模态正例的内模态难负例来解决这些问题，当应用于CLIP和FLIP等模型时，能在零样本检索和分类任务中带来持续的改进。 AI

影响 改进零样本检索和分类，可能增强多模态AI应用。

排序理由 关于视觉-语言预训练新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的SNAP方法通过合成负例增强视觉-语言预训练

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关于视觉-语言预训练新方法的学术论文。[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) · Nikos Giakoumoglou, Paschalis Giakoumoglou, Andreas Floros, Kleanthis Marios Papadopoulos, Tania Stathaki ·

    视觉语言预训练中的合成难负样本为何有效？

    arXiv:2610.09700v1 Announce Type: new Abstract: Synthetic hard negatives generated in the representation space have proven effective for unimodal self-supervised learning, but transferring this idea to vision-language pretraining is not straightforward. We analyze six representat…

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