Deep image hashing aims to map input images into simple binary hash codes via
deep neural networks and thus enable effective large-scale image retrieval.
Recently, hybrid networks that combine convolution and Transformer have
achieved superior performance on various computer tasks and have attracted
extensive attention from researchers. Nevertheless, the potential benefits of
such hybrid networks in image retrieval still need to be verified. To this end,
we propose a hybrid convolutional and self-attention deep hashing method known
as HybridHash. Specifically, we propose a backbone network with stage-wise
architecture in which the block aggregation function is introduced to achieve
the effect of local self-attention and reduce the computational complexity. The
interaction module has been elaborately designed to promote the communication
of information between image blocks and to enhance the visual representations.
We have conducted comprehensive experiments on three widely used datasets:
CIFAR-10, NUS-WIDE and IMAGENET. The experimental results demonstrate that the
method proposed in this paper has superior performance with respect to
state-of-the-art deep hashing methods. Source code is available
this https URL

通过深度神经网络将输入图像映射为简单的二进制哈希码，从而实现有效的大规模图像检索。我们提出了一种名为 HybridHash 的混合卷积和自注意力深度哈希方法，通过引入分阶段架构的骨干网络和块聚合函数来实现局部自注意力效果，并降低计算复杂性，以提高图像块之间的信息交流和视觉表示。实验结果表明，本文提出的方法在三个广泛使用的数据集上具有优越的性能，超过了现有的深度哈希方法。