Thanks to the great success of the deep learning, deep hashing for large-scale multimedia retrieval has made significant progress recently. However, most existing deep hashing algorithms suffer from slow convergence due to the gradient vanishing problem, caused by deep network structures and saturated activation functions. Moreover, a single convolution layer is often followed by down-sampling such as max pooling, resulting in local information loss that might affect the overall system robustness and performance. In this work, we propose a novel deep supervised hashing, Deep Loss Driven Multi-Scale Hashing (DLDMSH), which learns the high-quality approximate binary codes through an end-to-end network and improves the representative capacity of hash codes for large-scale image retrieval. Specifically, we design a Loss Driven Multi-Scale (LDMS) feature which is aggregated from convolutional feature maps. Moreover, a Pyramid Connected Convolutional Neural Network (PCNet) architecture is devised to generate LDMS feature, which inputs pairs of images during the training and outputs an image to approximate discrete values. In particular, 1×1 convolution kernels are applied to make a linear combination of features for realizing feature reduction, and the reduced features are fused in the fusion layer. This effectively improves the performance of deep features. A novel loss function preserving semantic information is integrated into an end-to-end learning scheme, which enhances the representative capacity of binary codes. Extensive experiments over four benchmark datasets show that DLDMSH significantly outperforms several other state-of-the-art hashing methods.

中文翻译：

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基于金字塔连接网络的深度损失驱动的多尺度哈希

由于深度学习的巨大成功，用于大规模多媒体检索的深度哈希最近取得了重大进展。然而，由于深度网络结构和饱和激活函数引起的梯度消失问题，大多数现有的深度哈希算法都存在收敛缓慢的问题。此外，单个卷积层后通常会进行下采样（例如最大池化），从而导致局部信息丢失，从而可能影响整个系统的鲁棒性和性能。在这项工作中，我们提出了一种新颖的深度监督散列，深度损失驱动的多尺度散列（DLDMSH），它通过端到端网络学习高质量的近似二进制代码，并提高散列码的代表容量- 尺度图像检索。具体来说，我们设计了一个由卷积特征图聚合而成的损失驱动多尺度 (LDMS) 特征。此外，设计了金字塔连接卷积神经网络 (PCNet) 架构来生成 LDMS 特征，该特征在训练期间输入图像对并输出图像以近似离散值。特别地，应用1×1卷积核对特征进行线性组合，实现特征约简，约简特征在融合层融合。这有效地提高了深度特征的性能。一种新的保留语义信息的损失函数被集成到端到端的学习方案中，这增强了二进制代码的代表能力。