The 2023 Video Similarity Dataset and Challenge
This provides a standardized evaluation framework for video copy detection and localization, addressing a domain-specific need in computer vision, though it is incremental as it builds on existing datasets and methods.
The authors introduced a dataset, benchmark, and challenge for video copy detection and localization, simulating a realistic needle-in-haystack setting with distractors, and proposed a metric to evaluate both tasks.
This work introduces a dataset, benchmark, and challenge for the problem of video copy detection and localization. The problem comprises two distinct but related tasks: determining whether a query video shares content with a reference video ("detection"), and additionally temporally localizing the shared content within each video ("localization"). The benchmark is designed to evaluate methods on these two tasks, and simulates a realistic needle-in-haystack setting, where the majority of both query and reference videos are "distractors" containing no copied content. We propose a metric that reflects both detection and localization accuracy. The associated challenge consists of two corresponding tracks, each with restrictions that reflect real-world settings. We provide implementation code for evaluation and baselines. We also analyze the results and methods of the top submissions to the challenge. The dataset, baseline methods and evaluation code is publicly available and will be discussed at a dedicated CVPR'23 workshop.