Datasets

List of publicly released InVID datasets

InVID FIVR-200K

Created by: A. M. P. Brasoveanu, L. J. B. Nixon, A. Weichselbraun (MODUL) G. Kordopatis-Zilos (CERTH-ITI / QMUL), S. Papadopoulos (CERTH-ITI), I. Patras (QMUL), Y. Kompatsiaris (CERTH-ITI)

Description: The InVID FIVR-200K dataset has been developed in the context of the InVID project with the aim of simulating the problem of Fine-grained Incident Video Retrieval (FIVR). FIVR is the problem where: given a query video, the objective is to retrieve all associated videos, considering several types of associations that range from duplicate videos to videos from the same incident. To address the benchmarking needs of such problem, the large-scale video dataset FIVR-200K has been constructed. It comprises 225,960 YouTube videos collected based on 4,687 major news events crawled from Wikipedia, and 100 video queries selected based on an automatic selection process. For the annotation of the dataset, an annotation protocol has been devised with respect to four types of video associations, i.e., Near-Duplicate Videos (ND), Duplicate Scene Videos (DS), Complementary Scene Videos (CS), and Incident Scene Videos (IS). To this end, FIVR-200K dataset contains the list of the collected Youtube ids, the crawled events from Wikipedia and the video annotations, which include the set of videos for each associations type for each query in the dataset.

Available at: @Zenodo, @Author’s website

StoryLens

Created by: A. M. P. Brasoveanu, L. J. B. Nixon, A. Weichselbraun (MODUL)

Description: The multistream corpora (StoryLens) created for Recognyze eval in InVID project. The current corpora contains annotations based on the following stream types: news (100 documents), twitter (200 documents), youtube (100 documents). The YouTube, Twitter and newsmedia documents are not provided with this corpus due to copyright reasons. The original documents can be retrieved by crawling their URLs (provided within the dataset). Concerning the underlying ontology, the focus is on location entities, therefore all types of conflicts between locations and other types of entities are included (see the datasets documentation for a complete list of the annotations taken into account when building the gold standard files). The Annotation Guideline is based on TAC and ACE guidelines. For further details about the dataset, please visit the link below.

Available at: @GitHub

InVID Fake Video Corpus 2018 (v3.0)

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

Description: The InVID TV Fake Video Corpus 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. This third version of the dataset, published near the end of the project, has been extended to include 200 fake videos and 180 real ones. Furthermore, using crawling and near-duplicate retrieval, a large number of near-duplicates of each fake and real video have been collected, leading to a total of 3957 videos annotated as fake and 2458 annotated as real. The videos are temporally ordered in cascades and accompanied by their metadata. As we do not own the rights to the videos, the dataset only contains the video URLs and annotations.

Available at: @Zenodo@Author’s website

InVID Fake Video Corpus v2.0

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

Description: This is the second version of the InVID Fake Video Corpus, containing 117 fake videos and 110 real videos, alongside annotations and descriptions. As we do not own the rights to the videos, the dataset only contains the video URLs and annotations.

Available at: @Zenodo

InVID Fake Video Corpus v1.0

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. The dataset does not aspire to serve as an exhaustive list of all forgeries that have circulated the Web in the past, but we intend to maintain and extend it throughout the course of the project as new cases arise. The collection is a collaborative effort between AFP and CERTH-ITI. 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 v2.0

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). See the README file for details. This is the second version of the dataset, including various corrections in annotation.

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