HybridHash: Hybrid Convolutional and Self-Attention Deep Hashing for Image Retrieval

TL;DR

HybridHash introduces a novel deep hashing method combining convolutional and self-attention mechanisms, achieving superior image retrieval performance on multiple datasets by enhancing local feature communication and reducing computational costs.

Contribution

This paper presents a hybrid convolutional and self-attention deep hashing framework with a stage-wise backbone and interaction module, improving retrieval accuracy over existing methods.

Findings

Outperforms state-of-the-art deep hashing methods on CIFAR-10, NUS-WIDE, and ImageNet.

Efficiently balances local attention and computational complexity.

Demonstrates the effectiveness of hybrid networks in image retrieval tasks.

Abstract

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.