The V3C1 Dataset: Advancing the State of the Art in Video Retrieval

Standardized datasets are of vital importance in multimedia research, as they form the basis for reproducible experiments and evaluations. In the area of video retrieval, widely used datasets such as the IACC, which has formed the basis for the TRECVID Ad-Hoc Video Search Task and other retrieval-related challenges, have started to show their age. For example, IACC is no longer representative of video content as it is found in the wild. This is illustrated by the figures below, showing the distribution of video age and duration across various datasets in comparison with a sample drawn from Vimeo and Youtube

V3C1 Dataset: An Evaluation of Content Characteristics

In this work we analyze content statistics of the V3C1 dataset, which is the first partition of the Vimeo Creative Commons Collection (V3C). The dataset has been designed to represent true web videos in the wild, with good visual quality and diverse content characteristics, and will serve as evaluation basis for the Video Browser Showdown 2019-2021 and TREC Video Retrieval (TRECVID) Ad-Hoc Video Search tasks 2019-2021. The dataset comes with a shot segmentation (around 1 million shots) for which we analyze content specifics and statistics. Our research shows that the content of V3C1 is very diverse, has no predominant characteristics and provides a low self-similarity. Thus it is very well suited for video retrieval evaluations as well as for participants of TRECVID AVS or the VBS

V3C1 Dataset: An Evaluation of Content Characteristics

In this work we analyze content statistics of the V3C1 dataset, which is the first partition of the Vimeo Creative Commons Collection (V3C). The dataset has been designed to represent true web videos in the wild, with good visual quality and diverse content characteristics, and will serve as evaluation basis for the Video Browser Showdown 2019-2021 and TREC Video Retrieval (TRECVID) Ad-Hoc Video Search tasks 2019-2021. The dataset comes with a shot segmentation (around 1 million shots) for which we analyze content specifics and statistics. Our research shows that the content of V3C1 is very diverse, has no predominant characteristics and provides a low self-similarity. Thus it is very well suited for video retrieval evaluations as well as for participants of TRECVID AVS or the VBS