FiGVCL: Fine-Grained Benchmark and Method for Video Copy Localization

Content-based video copy localization (VCL) aims to detect and locate copied segments in pairs of videos. VCL requires fine-grained video analysis to robustly identify copied segments that have been edited. Despite recent progress, the prohibitive cost of annotating copied segments and the lack of a fine-grained benchmark hinder the development of effective VCL systems. In this work, we annotate a new real-world dataset, FiGVCL, with challenging scenarios designed to evaluate VCL methods. FiGVCL is carefully annotated to preserve the temporal correspondences observed in copied segments. Moreover, we propose a novel fine-grained VCL benchmark metric based on temporal correspondences to improve discriminability. Finally, we design a simple but effective baseline model that uses fine-grained local embeddings for accurate copied segment localization. We also present an unsupervised training strategy that outperforms previous supervised VCL methods.