This paper addresses image copy detection, a task in online sharing platforms for copyright protection. While previous approaches have performed exceptionally well, the large size of their networks and descriptors remains a significant disadvantage, complicating their practical application. In this paper, we propose a novel method that achieves a competitive performance by using a lightweight network and compact descriptors. By utilizing relational self-supervised distillation to transfer knowledge from a large network to a small network, we enable the training of lightweight networks with a small descriptor size. Our approach, which we call Relational selfsupervised Distillation with Compact Descriptors (RDCD), introduces relational self-supervised distillation (RSD) for flexible representation in a smaller feature space and applies contrastive learning with a hard negative (HN) loss to prevent dimensional collapse. We demonstrate the effectiveness of our method using the DISC2021, Copydays, and NDEC benchmark datasets, with which our lightweight network with compact descriptors achieves a competitive performance. For the DISC2021 benchmark, ResNet-50/EfficientNet- B0 are used as a teacher and student respectively, the micro average precision improved by 5.0%/4.9%/5.9% for 64/128/256 descriptor sizes compared to the baseline method.

本文提出了一种名为RDCD的方法，使用一个轻量级网络和紧凑的描述符，在在线共享平台中用于图像复制检测和版权保护。通过利用关系自监督蒸馏将知识从大型网络转移至小型网络，我们使得训练轻量级网络成为可能，并引入了关系自监督蒸馏和具有硬负损失的对比学习，以防止维度坍缩。在DISC2021、Copydays和NDEC基准数据集上，我们的方法展现了竞争性的性能，其轻量级网络和紧凑描述符相比基线方法，对于64/128/256描述符大小，micro average precision 分别提高了5.0%/4.9%/5.9%。

基于紧凑描述符的图像复制检测的关系性自监督蒸馏