In this paper, we focus on triplet-based deep binary embedding networks for image retrieval task. The triplet loss has been shown to be most effective for the ranking problem. However, most of the previous works treat the triplets equally or select the hard triplets based on the loss. Such strategies do not consider the order relations, which is important for retrieval task. 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. 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. Extensive evaluations on four benchmark datasets show that the proposed method achieves significant performance compared with the state-of-the-art baselines.
翻译:在本文中,我们侧重于基于三重的深层双向嵌入网络,用于图像检索任务。 三重损失已证明对排名问题最为有效。 但是,大多数先前的工程对三重损失一视同仁或根据损失选择硬三重。 这些战略并不考虑对检索任务很重要的顺序关系。 为此,我们提出一个有秩序的重新加权方法,以有效培训基于三重深层网络,这些深重网络将重要三重和低重三重三重数据加起来。 首先,我们提出有秩序的加权因素,以显示三重因素的重要性,这些三重因素取决于二重代码的等级顺序。 然后,我们将三重损失调整成三重损失的平方三重损失,使损失功能对重要的三重数据进行更多的加权。 对四个基准数据集进行的广泛评价表明,与最先进的基线相比,拟议方法取得了显著的绩效。