Datasets

List of publicly released InVID datasets

InVID Fake Video Corpus

Created by: S. Papadopoulos, M. Zampoglou, I. Kompatsiaris (CERTH-ITI), D. Teyssou (AFP)

Description: The InVID TV Fake Video Corpus is a small collection of verified fake videos. It was developed in the context of the InVID project with the aim of gaining a perspective of the types of fake video that can be encountered in the real world. Currently the Corpus consists of 59 videos. For each video, information is provided describing the fake, its original source, and the evidence proving it is a fake. As we do not own the videos, the dataset only provides the video URLs and metadata, in the form of a tab-separated value (TSV) file.

Available at: @Zenodo

InVID TV Logo Dataset v1.0

Created by: O. Papadopoulou, M. Zampoglou, S. Papadopoulos, I. Kompatsiaris (CERTH-ITI)

Description: This dataset was created with the purpose of providing a training and evaluation benchmark for TV logo detection in videos. It contains the results from the segmentation and annotation of 2,749 YouTube videos originating from a large number of news TV channels. The videos have been annotated with respect to the TV channel logos they contain -specifically, by the name of the organization to which the logo belongs- and with shot boundary information. Furthermore, a set of logo templates has been extracted from the videos and organized alongside the corresponding channel information. As we do not own the rights to the videos, the dataset only contains the YouTube video IDs alongside the corresponding annotations. It further contains 503 logo template files and the corresponding metadata information (channel name, wikipedia link).

Available at: @Zenodo

Concept detection scores for the IACC.3 dataset (TRECVID AVS Task)

Created by: F. Markatopoulou, V. Mezaris (CERTH-ITI)

Description: This dataset contains the concept detection scores for the IACC.3 dataset (600 hours of internet archive videos), which is used in the TRECVID Ad-hoc Video Search (AVS) task. For further details about the use of this dataset and the re-production of the provided results can be found in the dataset’s description.

Available at: @Zenodo