Relational Self-Supervised Distillation with Compact Descriptors for Image Copy Detection

Abstract

Image copy detection is the task of detecting edited copies of any image within a reference database. While previous approaches have shown remarkable progress the large size of their networks and descriptors remains a dis-advantage complicating their practical application. In this paper we propose a novel method that achieves 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 smaller descriptor sizes. We introduce relational self-supervised distillation for flexible representation in a smaller feature space and apply contrastive learning with a hard negative loss to prevent dimensional collapse. For the DISC2021 benchmark ResNet-50 and EfficientNet-B0 are used as the teacher and student models respectively with micro average precision improving by 5.0%/4.9%/5.9% for 64/128/256 descriptor sizes compared to the baseline method. The code is available at https://github.com/juntae9926/RDCD.