Feature-space-time Coherence with Heterogeneous Data

• Grant number: 18/05668-3
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: May 01, 2018
• End date: April 30, 2021
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Anderson de Rezende Rocha
• Grantee: Bahram Lavi Sefidgari
• Host Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events, AP.TEM

Abstract

In this research, we are mainly aimed at answering the question of how to synchronize different sources of information (text content, posts, images and video recordings) in a comprehensive and coherent feature-space-time form for further inference, a problem we refer to as X-coherence. This question is precisely the first research question (Q1) of the DéjàVu thematic research project. The final expected outcomes are methods for performing X-coherence and combining different pieces of information in a comprehensive timeline for further inference and interpretation as well as solutions to transform heterogeneous sources of information into a unified representation.

Scientific publications

(6)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

LAVI, BAHRAM; NASCIMENTO, JOSE; ROCHA, ANDERSON; IEEE. SEMI-SUPERVISED FEATURE EMBEDDING FOR DATA SANITIZATION IN REAL-WORLD EVENTS. 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), v. N/A, p. 5-pg., 2021-01-01. (17/12646-3, 20/02241-9, 18/05668-3)

PADILHA, RAFAEL; ANDALO, FERNANDA A.; LAVI, BAHRAM; PEREIRA, LUIS A. M.; ROCHA, ANDERSON. Temporally sorting images from real-world events. PATTERN RECOGNITION LETTERS, v. 147, p. 212-219, JUL 2021. (17/21957-2, 18/16548-9, 18/05668-3, 17/12646-3)

RODRIGUES, CAROLINE MAZINI; SORIANO-VARGAS, AUREA; LAVI, BAHRAM; ROCHA, ANDERSON; DIAS, ZANONI. Manifold Learning for Real-World Event Understanding. IEEE Transactions on Information Forensics and Security, v. 16, p. 2957-2972, 2021. (18/16548-9, 18/16214-3, 17/16246-0, 15/11937-9, 18/05668-3, 13/08293-7, 17/12646-3, 17/16871-1)

PEIXOTO, BRUNO M.; LAVI, BAHRAM; DIAS, ZANONI; ROCHA, ANDERSON. Harnessing high-level concepts, visual, and auditory features for violence detection in videos. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, v. 78, JUL 2021. (17/12646-3, 18/05668-3)

PEIXOTO, BRUNO; LAVI, BAHRAM; BESTAGINI, PAOLO; DIAS, ZANONI; ROCHA, ANDERSON; IEEE. MULTIMODAL VIOLENCE DETECTION IN VIDEOS. 2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, v. N/A, p. 5-pg., 2020-01-01. (18/05668-3, 17/12646-3)

VEGA-OLIVEROS, DIDIER A.; NASCIMENTO, JOSE; LAVI, BAHRAM; ROCHA, ANDERSON. Real-world-events data sifting through ultra-small labeled datasets and graph fusion. APPLIED SOFT COMPUTING, v. 132, p. 17-pg., 2023-01-01. (17/12646-3, 20/02241-9, 19/26283-5, 18/05668-3)