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English(EN) Contrastive Learning for Image Complexity Representation

撤回的论文详述了用于图像复杂度的对比学习

一项已被撤回的研究论文介绍了CLIC(用于图像复杂度的对比学习),一个利用对比学习来表示图像复杂度的框架。该研究提出了随机裁剪和混合(RCM)方法,从多尺度局部图像裁剪中生成多样化的训练样本,旨在避免手动标注成本和人类主观偏见。实验表明,CLIC的性能与最先进的监督方法相当,并能提高计算机视觉任务的性能。 AI

影响 这项研究虽然已被撤回,但探索了图像复杂度表示的新颖方法,可能为未来的计算机视觉模型开发提供信息。

排序理由 该项目是一篇已被撤回的学术论文,详细介绍了一种新颖的图像复杂度表示方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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撤回的论文详述了用于图像复杂度的对比学习

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该项目是一篇已被撤回的学术论文,详细介绍了一种新颖的图像复杂度表示方法。[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) · Shipeng Liu, Liang Zhao, Dengfeng Chen, Zhanping Song ·

    Contrastive Learning for Image Complexity Representation

    arXiv:2408.03230v2 Announce Type: replace Abstract: Quantifying and evaluating image complexity can be instrumental in enhancing the performance of various computer vision tasks. Supervised learning can effectively learn image complexity features from well-annotated datasets. How…