To reduce the extreme label dependence of supervised product quantization methods, the semi-supervised paradigm usually employs massive unlabeled data to assist in regularizing deep networks, thereby improving model performance. However, the existing method focuses on the overall distribution consistency between unlabeled data and class prototypes, while ignoring subtle individual variances between unlabeled instances. Therefore, the local neighborhood structure is not fully explored, which will cause the model to easily overfit in the training set. In this paper, we introduce a new Fourier perspective to alleviate this issue by exploring the semantic relations between unlabeled instances in a self-supervised manner. Specifically, based on Fourier Transform, we first design a Phase Mixing (PM) strategy, which can manipulate the mixing area and values of the phase component between two images to control the proportion of semantic information. In this way, we can construct multi-level similarity neighbors naturally for unlabeled data. Then, a ranking quantization loss is formulated to perceive multi-level semantic variances in neighbor instances, which improves the robustness and generalization of the model. Extensive experiments in three different semi-supervised settings show that our method outperforms existing state-of-the-art methods by averaged 3.95% improvement on four datasets.

中文翻译：

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半监督图像检索的深度傅里叶排序量化

为了减少监督乘积量化方法的极端标签依赖性，半监督范式通常采用大量未标记数据来辅助对深度网络进行正则化，从而提高模型性能。然而，现有方法侧重于未标记数据和类原型之间的整体分布一致性，而忽略了未标记实例之间的细微个体差异。因此，局部邻域结构没有得到充分探索，这将导致模型在训练集中容易过拟合。在本文中，我们引入了一种新的傅立叶视角，通过以自我监督的方式探索未标记实例之间的语义关系来缓解这个问题。具体来说，基于傅里叶变换，我们首先设计了相位混合（PM）策略，它可以操纵两幅图像之间的混合区域和相位分量的值来控制语义信息的比例。这样，我们可以自然地为未标记的数据构建多级相似性邻居。然后，制定排名量化损失以感知相邻实例中的多级语义方差，从而提高模型的鲁棒性和泛化性。在三种不同的半监督设置中进行的大量实验表明，我们的方法在四个数据集上平均提高了 3.95%，优于现有的最先进方法。制定了排名量化损失以感知相邻实例中的多级语义差异，从而提高了模型的鲁棒性和泛化性。在三种不同的半监督设置中进行的大量实验表明，我们的方法在四个数据集上平均提高了 3.95%，优于现有的最先进方法。制定了排名量化损失以感知相邻实例中的多级语义差异，从而提高了模型的鲁棒性和泛化性。在三种不同的半监督设置中进行的大量实验表明，我们的方法在四个数据集上平均提高了 3.95%，优于现有的最先进方法。