In hash-based image retrieval systems, the transformed input from the original usually generates different codes, deteriorating the retrieval accuracy. To mitigate this issue, data augmentation can be applied during training. However, even if the augmented samples of one content are similar in real space, the quantization can scatter them far away in Hamming space. This results in representation discrepancies that can impede training and degrade performance. In this work, we propose a novel self-distilled hashing scheme to minimize the discrepancy while exploiting the potential of augmented data. By transferring the hash knowledge of the weakly-transformed samples to the strong ones, we make the hash code insensitive to various transformations. We also introduce hash proxy-based similarity learning and binary cross entropy-based quantization loss to provide fine quality hash codes. Ultimately, we construct a deep hashing framework that generates discriminative hash codes. Extensive experiments on benchmarks verify that our self-distillation improves the existing deep hashing approaches, and our framework achieves state-of-the-art retrieval results. The code will be released soon.

本文提出了一种基于数据增强的哈希检索方法，通过自我蒸馏来最小化哈希码与原始特征之间的差异，引入基于哈希代理的相似度学习和二元交叉熵损失来提供优质哈希码，构建了一个深度哈希框架，实现了目前最先进的检索结果。

图像检索的深层哈希蒸馏