这里把 zero-shot CLIP 和在 ResNet-50 的特征上做 linear probing SFT 训练的模型进行比较,CLIP 在通用一点的任务上表现都更好(绿色),而在比较专业的任务上表现差一些(蓝色)。 Few-shot classification CLIP 的特征也可以用 linear probing 的方式微调了做 few-shot classification。实验表明 CLIP 的表示比其他...
Tip-Adapter: 基于CLIP进行few-shot分类的免训练自适应方法。具体做法:以非参数的方式从few-shot训练集构建key-value缓存模型。 背景介绍 CLIP[1]提出从成对的自然语言监督中学习可迁移的视觉特征,进而无需重新训练,并展现出强大的零样本图像分类能力。 CoOp[2]在CLIP的基础上通过构建可学习的文本token对预训练的CL...
In this paper, we propose a training-free adaption method for CLIP to conduct few-shot classification, termed as Tip-Adapter, which not only inherits the training-free advantage of zero-shot CLIP but also performs comparably to those training-required approaches. Tip-Adapter constructs the adapter...
当K<10时,该任务被称为few-shot classification learning。相应地,对于K=1,我们称之为one-shot classification learning。如果我们使用所有可用的数据,这就是一个完全有监督的模型(老式的方法)。 图2:带有特征提取的图像分类(图片来自本文作者) 注意上文的关键词 “有监督的”——分类器应该事先知道类别标签。使...
The proposed few-shot fine-tuning framework is computationally efficient, robust to distribution shifts, and does not alter CLIP's parameters. We study the effectiveness of DAC by benchmarking on 11 widely used image classification tasks with consistent improvements in 16-shot classification upon ...
如果你选择了K-shot learning,那么在分类阶段的训练集应该只包含每个类别的K个实例。 当K<10时,该任务被称为few-shot classification learning。相应地,对于K=1,我们称之为one-shot classification learning。如果我们使用所有可用的数据,这就是一个完全有监督的模型(老式的方法)。
Thus, we ask the following question: can we achieve the best of both worlds, which not only takes the advantage of CLIP’s training-free property for zero-shot classification but also enjoys the strong performance of training-required methods for few-shot classification? Table 1: Comparison of...
Few-shot Classification 作者针对Finetuning EyeCLIP(称为'shot')中每个类别的有标签示例数量进行了调整,从n = 1, 2, 4, 8, 16不等,并在与全数据全模型微调分类相似的测试集上测试了模型。 Cross-Modal Retrieval 对于跨模态检索,作者采用了如上零样本分类的方法,检索与特定文本 Query (文本到图像检索)在对...
Domain Aligned CLIP for Few-shot Classification Large vision-language representation learning models like CLIP have demonstrated impressive performance for zero-shot transfer to downstream tasks while largely benefiting from inter-modal (image-text) alignment via contrastive objectives. This downstream ...
CVPR 2024·Ségolène Martin,Yunshi Huang,Fereshteh Shakeri,Jean-Christophe Pesquet,Ismail Ben Ayed· Transductive inference has been widely investigated in few-shot image classification but completely overlooked in the recent fast growing literature on adapting vision-langage models like CLIP. This pape...