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为了解决此问题，作者提出 Conditional Context Optimization (CoCoOp)。 CoCoOp在CoOp基础之上引入一个轻量级的神经网络为每张图像生成input-conditional tokens (vectors)，这些tokens会加上原本CoOp中的learnable vectors上。 提出Conditional Context Optimization（CoCoOp），通过进一步学习一个轻量级神经网络，为每个图像生成一个输入条件令牌（向量），与可学习的上下文向量相结合，使提示基于每个输入实例（图像）而不是固定不变，从而提高泛化能力。 为了解决这个问题，本文提出了条件上下文优化（CoCoOp），它通过学习一个轻量级的神经网络来扩展CoOp，为每个图像生成一个输入条件token（vector）。 与 CoOp 的静态提示相比，本文的动态提示会适应每个实例，因此对类偏移的敏感度较低。 大量实验表明，CoCoOp 比 CoOp 对不可见的类别的泛化效果要好得多，甚至显示出超越单个数据集的可转移性，并且还会产生更强的领域泛化性能。 最近在大规模的视觉-语言预训练方面的研究在zero-shot图像识别方面取得了惊人的性能，证明了在这种范式下学习开放世界视觉概念的潜力。 其中关键设计在于如何对视觉概念进行建模。

具体来说，CoOp技术通过将提示中的上下文词汇转化为可学习的向量集合，并仅需少量标注图像即可实现显著提升，这比人工调优的密集式提示效果更为突出。 在我们的研究中发现CoOp存在一个关键缺陷：所学上下文无法泛化到同一数据集内的更广泛未见类别，这表明CoOp在训练过程中过度拟合了基础类别的特征。 为解决这一问题，我们提出条件上下文优化（CoCoOp）方案。 该方案通过引入轻量级神经网络，为每张图像生成输入条件标记（向量），从而扩展了CoOp的功能。 与CoOp的静态提示不同，我们的动态提示能根据具体实例进行自适应调整，因此对类别偏移具有更强的鲁棒性。 大量实验表明，CoCoOp在处理未知类别时展现出远超CoOp的泛化能力，不仅在单一数据集上表现出色，更在跨领域迁移性能上更具优势。 To improve the generalization performance of optimized prompts, we propose the novel consistent prompt learning (cpl) approach that identifies and addresses the image distribution that causes prompt inconsistency by performing distributional exploration.

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