HybridHash：用于图像检索的混合卷积与自注意力深度哈希方法
HybridHash: Hybrid Convolutional and Self-Attention Deep Hashing for Image Retrieval
AI总结：
针对混合卷积与Transformer网络在图像检索中的潜力待验证问题，提出HybridHash深度哈希方法，通过分阶段骨干网络与交互模块优化表征，在三个主流数据集上性能超越现有最优深度哈希方法。
摘要：
深度图像哈希旨在通过深度神经网络将输入图像映射为简洁的二进制哈希码，从而实现高效的大规模图像检索。近年来，结合卷积与Transformer的混合网络在各类计算机任务中取得了优异性能，受到了研究人员的广泛关注。然而，这类混合网络在图像检索中的潜在优势仍有待验证。为此，我们提出了一种混合卷积与自注意力的深度哈希方法，名为HybridHash。具体而言，我们提出了一种具有分阶段架构的骨干网络，其中引入了块聚合函数以实现局部自注意力效果并降低计算复杂度。我们精心设计了交互模块，以促进图像块之间的信息交互并增强视觉表征。我们在三个广泛使用的数据集（CIFAR-10、NUS-WIDE和IMAGENET）上开展了全面实验。实验结果表明，本文提出的方法相较于现有最优深度哈希方法具备更优异的性能。源代码可在https://github.com/shuaichaochao/HybridHash获取。
英文摘要：
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 https://github.com/shuaichaochao/HybridHash.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
