The goal of supervised hashing is to construct hash mappings from collections of images and semantic annotations such that semantically relevant images are embedded nearby in the learned binary hash representations. Existing deep supervised hashing approaches that employ classification frameworks with a classification training objective for learning hash codes often encode class labels as one-hot or multi-hot vectors. We argue that such label encodings do not well reflect semantic relations among classes and instead, effective class label representations ought to be learned from data, which could provide more discriminative signals for hashing. In this article, we introduce Adaptive Labeling Deep Hashing (AdaLabelHash) that learns binary hash codes based on learnable class label representations. We treat the class labels as the vertices of a $K$ -dimensional hypercube, which are trainable variables and adapted together with network weights during the backward network training procedure. The label representations, referred to as codewords, are the target outputs of hash mapping learning. In the label space, semantically relevant images are then expressed by the codewords that are nearby regarding Hamming distances, yielding compact and discriminative binary hash representations. Furthermore, we find that the learned label representations well reflect semantic relations. Our approach is easy to realize and can simultaneously construct both the label representations and the compact binary embeddings. Quantitative and qualitative evaluations on several popular benchmarks validate the superiority of AdaLabelHash in learning effective binary codes for image search.

中文翻译：

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基于自适应标签表示学习二进制哈希码

监督散列的目标是从图像和语义注释的集合中构建散列映射，以便语义相关的图像被嵌入附近学习的二进制散列表示中。现有的深度监督散列方法采用具有分类训练目标的分类框架来学习散列码，通常将类标签编码为单热或多热向量。我们认为这样的标签编码不能很好地反映类之间的语义关系，相反，应该从数据中学习有效的类标签表示，这可以为散列提供更多的区分信号。在本文中，我们介绍了自适应标签深度哈希（AdaLabelHash），它基于可学习的类标签表示来学习二进制哈希码。我们将类标签视为一个顶点 $K$ 维超立方体，它们是可训练的变量，在后向网络训练过程中与网络权重一起调整。标签表示，称为码字，是哈希映射学习的目标输出。在标签空间中，语义相关的图像然后由附近关于汉明距离的码字表示，产生紧凑和有区别的二进制哈希表示。此外，我们发现学习的标签表示很好地反映了语义关系。我们的方法很容易实现，并且可以同时构建标签表示和紧凑的二进制嵌入。对几个流行基准的定量和定性评估验证了 AdaLabelHash 在学习用于图像搜索的有效二进制代码方面的优越性。