想要深入了解的同学,自行仔细阅读原论文即可。 如果说NSP-BERT这个模式,倒不是第一次出现,早前就有人提出用NLI模型来做Zero Shot的(参考《NLI Models as Zero-Shot Classifiers》),它的格式跟NSP是基本一致的,但需要标签语料有监督地微调,而纯无监督的NSP的利用,这还是第一次尝试。 实验效果 有意思的是,对于我...
我们引入一组虚拟类别,与之相关的语义嵌入为br,r=1,2,...,R.再次强调虚拟类别不对应于任何真实对象,它们被引入来提高模型灵活性。真实和虚拟类别形成一个权重双向图,权重被定义为: Σ−1是一个可以从数据中学习到参数,方便起见,我们设Σ=σ2I,通过交叉验证调整标量自由超参σ。并且可以应用马氏距离来进行度...
Zero-shot text classifiers based on label descriptions embed an input text and a set of labels into the same space: measures such as cosine similarity can then be used to select the most similar label description to the input text as the predicted label. In a true zero-shot setup, ...
这些关系可以进行 new categories 的 zero-shot classifiers 的学习。一个很有意思的问题是,我们想要探讨:if we can use structured information and complex relationships to learn visual classifiers without seeing any examples。 在本文当中,我们提出提取 隐式的知识表示(the implicit knowledge representation,i.e....
proposed a framework for creating zero-shot classifiers using natural language inference (NLI). The framework works by posing the sequence to be classified as an NLI premise and constructs a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs...
We apply the ranking-based representation to zero-shot learning problems, and present deterministic and probabilistic zero-shot classifiers which can be built from pre-trained classifiers without retraining. We demonstrate their the advantages on two large real-world image datasets. In particular, we ...
In traditional classifiers, the meaning of the labels is ignored (in fact, they're often simply discarded and replaced with integers internally). By contrast, CLIP creates an encoding of its classes and is pre-trained on over 400 million text to image pairs. This allows it to leverage transf...
Semantic segmentation models face limitations when it comes to scaling to a large number of object classes. In this paper, we introduce the task of zero-shot semantic segmentation, which involves learning pixel-wise classifiers for unseen object categories with no training examples. To ...
W^{cls}_i(这里i=1,...,S,即只考虑seen类的action category)则表示第i个可见类的action对应的特征向量,一个d \times 1的列向量,对应图上classifiers那一列的一个蓝色的圆柱体。这里W^{cls}_A则表示第A类对应的向量了。然后Z_{L,a1,o}^{ins}则表示对于a1这个video,其第o个attribute-feature的值,...
[4] Text-to-Image Diffusion Models are Zero-Shot Classifiers 标题:文本到图像扩散模型是零样本分类器 链接:arxiv.org/abs/2303.1523 代码:未开源 [5] WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation 标题:WinCLIP:零/少镜头异常分类和分割 链接:arxiv.org/abs/2303.1481 代码:未开源 [6]...