Mihai-Gabriel Constantin

Mihai-Gabriel Constantin
Polytechnic University of Bucharest | UPB

PhD

About

45
Publications
7,019
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
304
Citations
Introduction
I am a Ph.D. student from Romania, attending the Faculty of Electronics, Telecommunications and Information Technology, at Politehnica University of Bucharest, a member of LAPI (the Image Processing and Analysis Laboratory), in the Multimedia Lab, Research Center CAMPUS. My current domain of study is the automatic analysis of the visual impact of multimedia data, including but not limited to the prediction of multimedia data interestingness, memorability and aesthetics, the detection of violent visual content and some applications of these content analysis systems, such as media recommendation systems.

Publications

Publications (45)
Article
Full-text available
Understanding visual interestingness is a challenging task addressed by researchers in various disciplines ranging from humanities and psychology to, more recently, computer vision and multimedia. The rise of infographics and the visual information overload that we are facing today have given this task a crucial importance. Automatic systems are in...
Preprint
As of today, most movie recommendation services base their recommendations on collaborative filtering (CF) and/or content-based filtering (CBF) models that use metadata (e.g., genre or cast). In most video-on-demand and streaming services, however, new movies and TV series are continuously added. CF models are unable to make predictions in such a s...
Conference Paper
Full-text available
In this paper we propose a new dataset, i.e., the MMTF-14K multi-faceted dataset. It is primarily designed for the evaluation of video-based recommender systems, but it also supports the exploration of other multimedia tasks such as popularity prediction, genre classification and auto-tagging (aka tag prediction). The data consists of 13,623 Hollyw...
Chapter
Full-text available
The ability of multimedia data to attract and keep people’s interest for longer periods of time is gaining more and more importance in the fields of information retrieval and recommendation, especially in the context of the ever growing market value of social media and advertising. In this chapter we introduce a benchmarking framework (dataset and...
Conference Paper
Full-text available
In this article we analyze the prediction of image interestingness, a domain that is gaining importance in the fields such as recommendation systems, social media and advertising. We investigate the contribution of early and late fusion techniques, while using a set of image descriptors and analyze the best combinations that predict interestingness...
Conference Paper
Full-text available
The 2022 ImageCLEFfusion task is the first edition of this task, targeting the creation of late fusion or ensembling methods in two different scenarios: (i) the prediction of media visual interestingness, and (ii) social media image search results diversification. The objective proposed to participants is to train and test their proposed fusion sch...
Conference Paper
Full-text available
The ImageCLEFfusion task addresses the challenging task of creating ensembling schemes and algorithms for fusing a large set of inducers in two particular scenarios: a regression scenario where the ground truth data consists of media interestingness annotations and a search result diversification scenario. We present our team's approach for these t...
Chapter
Full-text available
This paper presents an overview of the ImageCLEF 2022 lab that was organized as part of the Conference and Labs of the Evaluation Forum – CLEF Labs 2022. ImageCLEF is an ongoing evaluation initiative (first run in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing infor...
Preprint
Full-text available
This paper describes the approach taken by the AI Multimedia Lab team for the MediaEval 2021 Predicting Media Memorability task. Our approach is based on a Vision Transformer-based learning method, which is optimized by filtering the training sets for the two proposed datasets. We attempt to train the methods we propose with video segments that are...
Chapter
Full-text available
In the context of the ever growing quantity of multimedia content from social, news and educational platforms, generating meaningful recommendations and ratings now requires a more advanced understanding of their impact on the user, such as their subjective perception. One of the important subjective concepts explored by researchers is visual inter...
Chapter
Full-text available
ImageCLEF is part of the Conference and Labs of the Evaluation Forum (CLEF) since 2003. CLEF 2022 will take place in Bologna, Italy. ImageCLEF is an ongoing evaluation initiative which promotes the evaluation of technologies for annotation, indexing, and retrieval of visual data with the aim of providing information access to large collections of i...
Preprint
Full-text available
This paper describes the MediaEval 2021 Predicting Media Memorability}task, which is in its 4th edition this year, as the prediction of short-term and long-term video memorability remains a challenging task. In 2021, two datasets of videos are used: first, a subset of the TRECVid 2019 Video-to-Text dataset; second, the Memento10K dataset in order t...
Preprint
Full-text available
Using a collection of publicly available links to short form video clips of an average of 6 seconds duration each, 1,275 users manually annotated each video multiple times to indicate both long-term and short-term memorability of the videos. The annotations were gathered as part of an online memory game and measured a participant's ability to recal...
Article
Full-text available
Using a collection of publicly available links to short form video clips of an average of 6 seconds duration each, 1275 users manually annotated each video multiple times to indicate both long-term and short-term memorability of the videos. The annotations were gathered as part of an online memory game and measured a participant’s ability to recall...
Chapter
Full-text available
This paper presents an overview of the ImageCLEF 2021 lab that was organized as part of the Conference and Labs of the Evaluation Forum – CLEF Labs 2021. ImageCLEF is an ongoing evaluation initiative (first run in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing infor...
Article
Full-text available
This paper presents an overview of the ImageCLEF 2021 lab that was organized as part of the Conference and Labs of the Evaluation Forum-CLEF Labs 2021. ImageCLEF is an ongoing evaluation initiative (first run in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing informa...
Article
Full-text available
In this paper, we report on the creation of a publicly available, common evaluation framework for image and video visual interestingness prediction. We propose a robust data set, the Interestingness10k, with 9831 images and more than 4 h of video, interestigness scores determined based on more than 1M pair-wise annotations of 800 trusted annotators...
Chapter
Full-text available
This paper presents the ideas for the 2021 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum—CLEF Labs 2021 in Bucharest, Romania. ImageCLEF is an ongoing evaluation initiative (active since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the...
Conference Paper
Full-text available
While ensemble systems and late fusion mechanisms have proven their effectiveness by achieving state-of-the-art results in various computer vision tasks, current approaches are not exploiting the power of deep neural networks as their primary ensembling algorithm, but only as inducers, i.e., systems that are used as inputs for the primary ensemblin...
Article
Full-text available
This paper presents the ideas for the 2021 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum-CLEF Labs 2021 in Bucharest, Romania. ImageCLEF is an ongoing evaluation initiative (active since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the...
Preprint
Full-text available
This paper describes the MediaEval 2020 \textit{Predicting Media Memorability} task. After first being proposed at MediaEval 2018, the Predicting Media Memorability task is in its 3rd edition this year, as the prediction of short-term and long-term video memorability (VM) remains a challenging task. In 2020, the format remained the same as in previ...
Chapter
This paper presents an overview of the ImageCLEF 2020 lab that was organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2020. ImageCLEF is an ongoing evaluation initiative (first run in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing infor...
Article
Full-text available
Building accurate knowledge of the identity, the geographic distribution and the evolution of species is essential for the sustainable development of humanity, as well as for biodiversity conservation. However , the difficulty of identifying plants and animals in the field is hindering the aggregation of new data and knowledge. Identifying and nami...
Article
Full-text available
This paper presents an overview of the ImageCLEF 2020 lab that was organized as part of the Conference and Labs of the Evaluation Forum-CLEF Labs 2020. ImageCLEF is an ongoing evaluation initiative (first run in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing informa...
Conference Paper
Full-text available
Deep neural networks (DNNs) are universal estimators that have achieved state-of-the-art performance in a broad spectrum of classification tasks, opening new perspectives for many applications. One of them is addressing ensemble learning. In this paper, we introduce a set of deep learning techniques for ensemble learning with dense, attention, and...
Article
MediaEval is a benchmarking initiative that offers challenges in multimedia retrieval, analysis and exploration. The tasks offered by MediaEval concentrate specifically on the human and social aspects of multimedia. They encourage researchers to bring together multiple modalities (visual, text, audio) and to think in terms of systems that serve use...
Article
Full-text available
In this paper, we report on the creation of a publicly available, common evaluation framework, for Violent Scenes Detection (VSD) in Hollywood and YouTube videos. We propose a robust data set, the VSD96, with more than 96 hours of video of various genres, annotations at different levels of detail (e.g., shot-level, segment-level), annotations of mi...
Chapter
This paper presents an overview of the 2020 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum—CLEF Labs 2020 in Thessaloniki, Greece. ImageCLEF is an ongoing evaluation initiative (run since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the...
Conference Paper
Full-text available
This paper presents an overview of the 2020 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum-CLEF Labs 2020 in Thessaloniki, Greece. ImageCLEF is an ongoing evaluation initiative (run since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the...
Conference Paper
Full-text available
In this working note paper we present the contribution and results of the participation of the UPB-L2S team to the MediaEval 2019 Predicting Media Memorability Task. The task requires participants to develop machine learning systems able to predict automatically whether a video will be memorable for the viewer, and for how long (e.g., hours, or day...
Conference Paper
Full-text available
In this paper, we present the Predicting Media Memorability task, which is running for the second year at the MediaEval 2019 Benchmarking Initiative for Multimedia Evaluation. Participants are required to create systems that are able to automatically predict the memorability scores of a collection of videos, which should represent the "short-term"...
Article
Full-text available
As of today, most movie recommendation services base their recommendations on collaborative filtering (CF) and/or content-based filtering (CBF) models that use metadata (e.g., genre or cast). In most video-on-demand and streaming services, however, new movies and TV series are continuously added. CF models are unable to make predictions in such a s...
Conference Paper
Full-text available
We propose a multi-modal content-based movie recommender system that replaces human-generated metadata with content descriptions automatically extracted from the visual and audio channels of a video. Content descriptors improve over traditional metadata in terms of both richness (it is possible to extract hundreds of meaningful features covering va...
Preprint
Full-text available
In this paper we introduce the MediaEval 2018 task Recommending Movies Using Content. It focuses on predicting global scores of users given to movies, i.e., average rating (representing global appreciation of the movies by the viewers) and the rating variance (representing agreement/disagreement between users) using audio, visual and textual featur...
Conference Paper
Full-text available
In the following paper we will present our contribution, approach and results for the MediaEval 2017 Predicting Media Interestingness task. We studied several visual descriptors and created several early and late fusion approaches in our machine learning system, optimized for best results for this benchmarking competition.
Conference Paper
Full-text available
In this paper we present the results achieved during the 2016 MediaEval Retrieving Diverse Social Images Task, using an approach based on pseudo-relevance feedback, in which human feedback is replaced by an automatic selection of images. The proposed approach is designed to have in priority the diversification of the results , in contrast to most o...
Conference Paper
Full-text available
This paper will present our results for the MediaEval 2016 Predicting Media Interestingness task. We proposed an approach based on video descriptors and studied several machine learning models, in order to detect the optimal configuration and combination for the descriptors and algorithms that compose our system.

Network

Cited By

Projects

Projects (2)
Project
ImageCLEF 2023 is an evaluation campaign that is being organized as part of the CLEF initiative labs. The campaign offers several research tasks that welcome participation from teams around the world. The results of the campaign appear in the working notes proceedings, published by CEUR Workshop Proceedings (CEUR-WS.org). Selected contributions among the participants will be invited for publication in the following year in the Springer Lecture Notes in Computer Science (LNCS) together with the annual lab overviews.
Project
Study computer vision and machine learning techniques for predicting multimedia interestingness in particular and visual impact of multimedia data in general.