TRECVID 2019: An Evaluation Campaign to Benchmark Video Activity Detection, Video Captioning and Matching, and Video Search & Retrieval
| Autor: | Awad, G., Butt, A. A., Keith Curtis, Lee, Y., Fiscus, J., Godil, A., Delgado, A., Zhang, J., Godard, E., Diduch, L., Smeaton, A. F., Graham, Y., Kraaij, W., Quénot, G. |
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| Přispěvatelé: | Georgetown University [Washington] (GU), National Institute of Standards and Technology [Gaithersburg] (NIST), Johns Hopkins University (JHU), Dakota Consulting Inc. [Silver Spring], Dublin City University (DCU), Ireland's university of Enterprise, Leiden University, Modélisation et Recherche d’Information Multimédia [Grenoble] (MRIM), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF) |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR] Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] ComputingMilieux_MISCELLANEOUS [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
| Zdroj: | TREC Video Retrieval Evaluation: TRECVID TREC Video Retrieval Evaluation: TRECVID, Nov 2019, Gaithersburg, United States Scopus-Elsevier 2019 TREC Video Retrieval Evaluation, TRECVID 2019, 2019 TREC Video Retrieval Evaluation, TRECVID 2019, 12 November 2019 through 13 November 2019 |
| DOI: | 10.48550/arxiv.2009.09984 |
| Popis: | The TREC Video Retrieval Evaluation (TRECVID) 2019 was a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in research and development of content-based exploitation and retrieval of information from digital video via open, metrics-based evaluation. Over the last nineteen years this effort has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. TRECVID has been funded by NIST (National Institute of Standards and Technology) and other US government agencies. In addition, many organizations and individuals worldwide contribute significant time and effort. TRECVID 2019 represented a continuation of four tasks from TRECVID 2018. In total, 27 teams from various research organizations worldwide completed one or more of the following four tasks: 1. Ad-hoc Video Search (AVS) 2. Instance Search (INS) 3. Activities in Extended Video (ActEV) 4. Video to Text Description (VTT) This paper is an introduction to the evaluation framework, tasks, data, and measures used in the workshop. Comment: TRECVID Workshop overview paper. 39 pages |
| Databáze: | OpenAIRE |
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