  Image copy detection is a task of detecting edited copies from any image
within a reference database. While previous approaches have shown remarkable
progress, the large size of their networks and descriptors remains
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. We introduce relational self-supervised distillation for
flexible representation in a smaller feature space and applies contrastive
learning with a hard negative loss to prevent dimensional collapse. 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.
