In this paper, we focus on triplet-based deep binary embedding networks for image retrieval task. The triplet loss has been shown to be effective for hashing retrieval. However, most of the triplet-based deep networks treat the triplets equally or select the hard triplets based on the loss. Such strategies do not consider the order relations of the binary codes and ignore the hash encoding when learning the feature representations. To this end, we propose an order-aware reweighting method to effectively train the triplet-based deep networks, which up-weights the important triplets and down-weights the uninformative triplets via the rank lists of the binary codes. First, we present the order-aware weighting factors to indicate the importance of the triplets, which depend on the rank order of binary codes. Then, we reshape the triplet loss to the squared triplet loss such that the loss function will put more weights on the important triplets. The extensive evaluations on several benchmark datasets show that the proposed method achieves significant performance compared with the state-of-the-art baselines.

中文翻译：

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通过三元组的顺序感知重加权改进深度二进制嵌入网络

在本文中，我们专注于用于图像检索任务的基于三元组的深度二进制嵌入网络。三元组损失已被证明对哈希检索是有效的。然而，大多数基于三元组的深度网络平等对待三元组或根据损失选择硬三元组。这种策略在学习特征表示时不考虑二进制代码的顺序关系并忽略哈希编码。为此，我们提出了一种顺序感知重加权方法来有效训练基于三元组的深度网络，该方法通过二进制代码的等级列表增加重要三元组的权重并降低无信息三元组的权重。首先，我们提出了顺序感知加权因子来指示三元组的重要性，这取决于二进制代码的排序顺序。然后，我们将三元组损失重塑为平方三元组损失，以便损失函数将更多的权重放在重要的三元组上。对几个基准数据集的广泛评估表明，与最先进的基线相比，所提出的方法取得了显着的性能。