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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Manifold Learning for Real-World Event Understanding

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Rodrigues, Caroline Mazini [1] ; Soriano-Vargas, Aurea [1] ; Lavi, Bahram [1] ; Rocha, Anderson [1] ; Dias, Zanoni [1]
Total Authors: 5
[1] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas - Brazil
Total Affiliations: 1
Document type: Journal article
Source: IEEE Transactions on Information Forensics and Security; v. 16, p. 2957-2972, 2021.
Web of Science Citations: 0

Information coming from social media is vital to the understanding of the dynamics involved in multiple events such as terrorist attacks and natural disasters. With the spread and popularization of cameras and the means to share content through social networks, an event can be followed through many different lenses and vantage points. However, social media data present numerous challenges, and frequently it is necessary a great deal of data cleaning and filtering techniques to separate what is related to the depicted event from contents otherwise useless. In a previous effort of ours, we decomposed events into representative components aiming at describing vital details of an event to characterize its defining moments. However, the lack of minimal supervision to guide the combination of representative components somehow limited the performance of the method. In this paper, we extend upon our prior work and present a learning-from-data method for dynamically learning the contribution of different components for a more effective event representation. The method relies upon just a few training samples (few-shot learning), which can be easily provided by an investigator. The obtained results on real-world datasets show the effectiveness of the proposed ideas. (AU)

FAPESP's process: 18/16548-9 - Learning Visual Clues of the Passage of Time
Grantee:Luis Augusto Martins Pereira
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 18/16214-3 - Heterogeneous data analysis for event detection
Grantee:Caroline Mazini Rodrigues
Support type: Scholarships in Brazil - Master
FAPESP's process: 17/16246-0 - Sensitive media analysis through deep learning architectures
Grantee:Sandra Eliza Fontes de Avila
Support type: Regular Research Grants
FAPESP's process: 15/11937-9 - Investigation of hard problems from the algorithmic and structural stand points
Grantee:Flávio Keidi Miyazawa
Support type: Research Projects - Thematic Grants
FAPESP's process: 18/05668-3 - Feature-space-time Coherence with Heterogeneous Data
Grantee:Bahram Lavi Sefidgari
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 13/08293-7 - CCES - Center for Computational Engineering and Sciences
Grantee:Munir Salomao Skaf
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events
Grantee:Anderson de Rezende Rocha
Support type: Research Projects - Thematic Grants
FAPESP's process: 17/16871-1 - Problems of sorting permutations by fragmentation-weighted operations
Grantee:Alexsandro Oliveira Alexandrino
Support type: Scholarships in Brazil - Master