Duc‐Tien Dang‐Nguyen

University of Bergen

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Co-Authors

Cathal Gurrin

Dublin City University

Michael A. Riegler

Simula Metropolitan Center for Digital Engineering

Minh–Triet Tran

Vietnam National University Ho Chi Minh City

Pål Halvorsen

Simula Metropolitan Center for Digital Engineering

Giulia Boato

University of Trento

Mathias Lux

University of Klagenfurt

Liting Zhou

Dublin City University

Minh-Son Dao

National Institute of Information and Communications Technology

Francesco G. B. De Natale

University of Trento

Luca Piras

University of Cagliari

Cooperative Institutions

University of Bergen

Simula Metropolitan Center for Digital Engineering

Vietnam National University Ho Chi Minh City

Ho Chi Minh City University of Science

Dublin City University

University of Oslo

Simula Research Laboratory

University of Trento

UiT The Arctic University of Norway

OsloMet – Oslo Metropolitan University

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Building a Disclosed Lifelog Dataset

Duc‐Tien Dang‐Nguyen Liting Zhou Rashmi Gupta Michael Riegler Cathal Gurrin

In this paper, we address the challenge of how to build a disclosed lifelog dataset by proposing the principles for building and sharing such types of data. Based on the proposed principles, we describe processes for how we built the benchmarking lifelog dataset for NTCIR-13 - Lifelog 2 tasks. Further, a list of potential applications and a framework for anonymisation are proposed and discussed.

Lifelog

Benchmarking

10.1145/3095713.3095736

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Citations (17)

The CERTH-UNITN Participation @ Verifying Multimedia Use 2015

MediaEval (2015)

Christina Boididou Symeon Papadopoulos Duc‐Tien Dang‐Nguyen Giulia Boato Ioannis Kompatsiaris

We propose an approach that predicts whether a tweet, which is accompanied by multimedia content (image/video), is trustworthy or deceptive. We test dierent combinations of quality and trust-oriented features (tweet-based, userbased and forensics) in tandem with a standard classication and an agreement-retraining technique, with the goal of predicting the most likely label (fake or real) for each tweet. The experiments carried out on the Verifying Multimedia Use dataset show that the best performance is achieved when using all available features in combination with the agreement-retraining method.

Retraining

Trustworthiness

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Citations (16)

Multimedia AI vs Information Disorder: A Journey of Discovery

Duc‐Tien Dang‐Nguyen

In this talk, Dang-Nguyen discusses case studies conducted in collaboration with fact-checkers and journalists in the Nordic countries, with a specific focus on the user needs of fact-checkers, their workflows, and tools employed for verifying visual content in news (see [1, 2]). The majority of the case study centers on examining the workflows and tools utilized by multiple fact-checkers at Faktisk (faktisk.no), a prominent Norwegian fact-checking organization. The discussion covers the efficacy of AI tools, geolocation services, internet archives, and social media monitoring platforms in facilitating efficient evidence processing and verification. This talk involves sharing experiences and interactive discussions with the audience, encouraging exploration of the potential benefits and current limitations of technology, aiming to provide insights for future developments in multimedia AI.

Geolocation

Norwegian

10.1145/3643491.3663508

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Citations (0)

ICDAR’23: Intelligent Cross-Data Analysis and Retrieval

Guillaume Habault Minh-Son Dao Michael A. Riegler Duc‐Tien Dang‐Nguyen Yuta Nakashima

Recently, there has been an increased interest in cross-data research problems, such as predicting air quality using life logging images, predicting congestion using weather and tweets data, and predicting sleep quality using daily exercises and meals. Although several research focusing on multimodal data analytics have been performed, few studies have been conducted on cross-data research (e.g., cross-modal data, cross-domain, cross-platform). The article collection "Intelligent Cross-Data Analysis and Retrieval" aims to encourage research in intelligent cross-data analytics and retrieval and contribute to the creation of a sustainable society. Researchers from diverse domains such as well-being, disaster prevention and mitigation, mobility, climate, tourism and healthcare are welcome to contribute to this Research Topic.

Data Analysis

Social media analytics

Sentiment Analysis

Data aggregator

10.1145/3591106.3592302

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Citations (1)

GameStory Task at MediaEval 2019.

MediaEval (2019)

Mathias Lux Michael A. Riegler Duc‐Tien Dang‐Nguyen Johanna Pirker Martin Potthast

Source

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Citations (3)

An Interactive Lifelog Retrieval System for Activities of Daily Living Understanding.

CLEF (Working Notes) (2018)

Liting Zhou Luca Piras Michael Riegler Mathias Lux Duc‐Tien Dang‐Nguyen

Lifelog

Source

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Citations (5)

Exploiting visual search theory to infer social interactions

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2013)

Paolo Rota Duc‐Tien Dang‐Nguyen Nicola Conci Nicu Sebe

In this paper we propose a new method to infer human social interactions using typical techniques adopted in literature for visual search and information retrieval. The main piece of information we use to discriminate among different types of interactions is provided by proxemics cues acquired by a tracker, and used to distinguish between intentional and casual interactions. The proxemics information has been acquired through the analysis of two different metrics: on the one hand we observe the current distance between subjects, and on the other hand we measure the O-space synergy between subjects. The obtained values are taken at every time step over a temporal sliding window, and processed in the Discrete Fourier Transform (DFT) domain. The features are eventually merged into an unique array, and clustered using the K-means algorithm. The clusters are reorganized using a second larger temporal window into a Bag Of Words framework, so as to build the feature vector that will feed the SVM classifier.

Proxemics

Sliding window protocol

Feature vector

10.1117/12.2005307

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Citations (0)

A Baseline Interactive Retrieval Engine for Visual Lifelogs at the NTCIR-14 Lifelog-3 Task

Lecture notes in computer science (2019)

Van-Tu Ninh Tu-Khiem Le Liting Zhou Graham Healy Kaushik Venkataraman

Lifelog

Baseline (sea)

10.1007/978-3-030-36805-0_3

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Citations (4)

A Deep Learning based Food Recognition System for Lifelog Images

Binh T. Nguyen Duc‐Tien Dang‐Nguyen Tien Dang Thai Van Phat Cathal Gurrin

In this paper, we propose a deep learning based system for food recognition from personal life archive images.The system first identifies the eating moments based on multi-modal information, then tries to focus and enhance the food images available in these moments, and finally, exploits GoogleNet as the core of the learning process to recognise the food category of the images.Preliminary results, experimenting on the food recognition module of the proposed system, show that the proposed system achieves 95.97% classification accuracy on the food images taken from the personal life archive from several lifeloggers, which potentially can be extended and applied in broader scenarios and for different types of food categories.

Lifelog

10.5220/0006749006570664

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Citations (14)

Verifying information with multimedia content on twitter

Multimedia Tools and Applications (2017)

Christina Boididou Stuart E. Middleton Zhiwei Jin Symeon Papadopoulos Duc‐Tien Dang‐Nguyen

Benchmark (surveying)

10.1007/s11042-017-5132-9

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Citations (62)