Anyway,CoOp不算完美,但也实实在在是第一个在vision-language pretrained model中用prompt learning,对community的贡献不可否认,之后有机会会介绍CoOp的后续之作-- CoCoOp[2]. 参考 ^Learning Transferable Visual Models From Natural Language Supervisionhttps://arxiv.org/pdf/2103.00020.pdf ^Conditional Prompt Le...
Vision - Language Models:CLIP等模型通过对比学习将图像和文本映射到公共嵌入空间,早期工作在文本特征提取和图像文本匹配方面技术不同,本文旨在促进此类模型在下游数据集的适应和部署。 Prompt Learning in NLP:NLP中通过提示学习诱导预训练语言模型生成答案,相关方法包括通过文本挖掘和释义生成候选提示、基于梯度的方法以及...
learning that is based mostly on discretized labels, vision-language pre-training aligns images and texts in a common feature space, which allows zero-shot transfer to a downstream task via prompting, i.e., classification weights are synthesized from natural language describing classes of interest....
把人工设定的 prompt 替换为 learnable 的 prompt: ●[CLASS] 放在后面: ● [CLASS] 放在中间: Prompt 可以在不同类之间公用,也可以为每个类使用不同的 prompts(对于细粒度分类任务更有效)。 ▲ Learning to Prompt for Vision-Language Mod...
实验结果表明，CSC结构的效果平平，而类比信息位于中间，头尾为可变token时，效果最佳。长prompt长度有助于更充分地学习信息。然而，基础结构的影响规律并不明显。对于学习到的词进行解释时，通过反向映射回词表发现，这些词组合起来并无实际意义。此研究的出发点是探索可学习的token化prompt，实践证明效果尚可...
language models for downstream image recognition. Concretely, CoOp models a prompt's context words with learnable vectors while the entire pre-trained parameters are kept fixed. To handle different image recognition tasks, we provide two implementations of CoOp: unified context and class-specific ...
▲ Learning to Prompt for Vision-Language Model Conditional Prompt Learning for Vision-Language Models[11] CoOp 在泛化到新的类别上时性能不好。 ▲ To learn generalizable prompts 所以把 prompt 设计为 instance-conditional 的。 ▲ To learn generalizable prompts ...
▲ Prompt engineering vs Context Optimization (CoOp) 把人工设定的 prompt 替换为 learnable 的 prompt: ●[CLASS] 放在后面: ● [CLASS] 放在中间: Prompt 可以在不同类之间公用,也可以为每个类使用不同的 prompts(对于细粒度分类任务更有效)。 ▲ Learning to Prompt for Vision-Language Model ...
CoOp: Learning to Prompt for Vision-Language Models CoOp的motivation如上图所示:CLIP是固定prompt:a photo of a [class],但是不同prompt的影响影响很大,比如从图a可以看出,少了一个a,acc直接低了6个点。每次根据人工设计prompt太麻烦,设计的还不一定好,那怎么办呢?熟悉nlp的prompt的小伙伴应该脱口而出了,应...
Foundational vision-language models such as CLIP are becoming a new paradigm in vision, due to their excellent generalization abilities. However, adapting ... MU Khattak,MF Naeem,M Naseer,... 被引量: 0发表: 2024年 Multi-task prompt tuning with soft context sharing for vision–language models...