论文标题
通过图像引导的语义分类零射击识别
Zero-Shot Recognition through Image-Guided Semantic Classification
论文作者
论文摘要
我们提出了一个新的基于嵌入的框架，用于零光学习（ZSL）。大多数基于嵌入的方法旨在学习每个类的图像分类器（视觉表示）及其类原型（语义表示）之间的对应关系。通过用于多标签分类的二进制相关方法的动机，我们建议在图像和语义分类器之间成型地学习映射。给定输入图像，提出的图像引导的语义分类（IGSC）方法会创建标签分类器，将其应用于所有标签嵌入式，以确定标签是否属于输入图像。因此，语义分类器是图像适应性的，并在推理过程中生成。 IGSC在概念上很简单，可以通过对现有深层体系结构进行分类的略有增强来实现；然而，在标准基准上，这是有效的，并且优于最先进的基于嵌入的广义ZSL方法。
We present a new embedding-based framework for zero-shot learning (ZSL). Most embedding-based methods aim to learn the correspondence between an image classifier (visual representation) and its class prototype (semantic representation) for each class. Motivated by the binary relevance method for multi-label classification, we propose to inversely learn the mapping between an image and a semantic classifier. Given an input image, the proposed Image-Guided Semantic Classification (IGSC) method creates a label classifier, being applied to all label embeddings to determine whether a label belongs to the input image. Therefore, semantic classifiers are image-adaptive and are generated during inference. IGSC is conceptually simple and can be realized by a slight enhancement of an existing deep architecture for classification; yet it is effective and outperforms state-of-the-art embedding-based generalized ZSL approaches on standard benchmarks.
