MediaEval 2016 - Emotional Impact of Movies Task

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The document describes the MediaEval 2016 Emotional Impact of Movies Task. It involves predicting the emotional impact of movies in terms of induced valence and arousal. There are two subtasks: predicting emotion for short video clips and predicting continuous emotion over longer videos on a per-second basis. The task uses datasets with ground truth emotion ratings collected through crowdsourcing and surveys to evaluate submissions. The goal is to build models and use multimedia features to automatically predict the emotional impact of movies.

The MediaEval 2016 Emotional Impact of Movies Task
Run submissions
• Up to 5 runs for each subtask
• A required run which uses no external
training data, only the provided development
data is allowed
Evaluation Metrics:
• Mean Square Error
• Pearson’s Correlation Coefficient
Development dataset: LIRIS-ACCEDE
Discrete LIRIS-ACCEDE
• 9800 video clips from 160 movies under Creative Commons licenses
• Duration between 8s and 12s
• Cross-validated through a controlled experimental protocol
Continuous LIRIS-ACCEDE
• 30 movies
• Duration between 117s and 4,566s (total duration: ~7 hours)
• Continuous induced valence and arousal self-assessments
Test dataset:
• From 49 new movies under Creative Commons licenses
• 1,200 additional short video clips for the first subtask (between 8 and 12 seconds)
• 10 additional long movies (from 25 minutes to 1 hour and 35 minutes) for the second subtask (for a total
duration of 11.48 hours)
Sqdf
sdf
Ground truth
Valence and arousal ranking:
• Pairwise video comparisons on CrowdFlower
• Annotators asked to focus on the emotion they felt
• Simple task:
• Which one conveys the most positive emotion?
• Which one conveys the calmest emotion?
From rankings to ratings:
• Ratings collected for 40 video clips regularly distributed
• 28 participants
• Ratings estimated using Gaussian Process models
Continuous annotation:
• Induced valence and arousal self-assessments
• 16 participants
• Modified Gtrace interface and joystick
Task Description
• Deploy multimedia features and models to automatically predict the emotional impact of movies
• Emotion considered in terms of induced valence and arousal
Two subtasks:
• Global emotion prediction: given a short video clip (around 10 seconds), participants’ systems are expected to
predict a score of induced valence (negative-positive) and induced arousal (calm-excited) for the whole clip;
• Continuous emotion prediction: as an emotion felt during a scene may be influenced by the emotions felt during
the previous ones, the purpose here is to consider longer videos, and to predict the valence and arousal
continuously along the video. Thus, a score of induced valence and arousal should be provided for each 1s-
segment of the video.
Context
• An evolution of previous years tasks on violence and affect prediction from videos
• Applications:
• Personalized content delivery
• Movie recommendation
• Video editing supervision
• Video summarization
• Protection of children from potential harmful content
Organizers: Emmanuel Dellandréa, Liming Chen, Yoann Baveye, Mats Sjöberg, Christel Chamaret
Contact: Emmanuel Dellandréa – emmanuel.dellandrea@ec-lyon.fr
Representation of emotions
Credits and license information is available here:
http://liris-accede.ec-lyon.fr/database.php
Arousal Valence

Presenter: Cynthia Liem TUD-MMC at MediaEval 2016: Predicting Media Interestingness Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Cynthia Liem Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_30.pdf Video: https://youtu.be/NQan10E_-kE Abstract: This working notes paper describes the TUD-MMC entry to the MediaEval 2016 Predicting Media Interestingness Task. Noting that the nature of movie trailer shots is different from that of preceding tasks on image and video interestingness, we propose two baseline heuristic approaches based on the clear occurrence of people. MAP scores obtained on the development set and test set suggest that our approaches cover a limited but non-marginal subset of the interestingness spectrum. Most strikingly, our obtained scores on the Image and Video Subtasks are comparable or better than those obtained when evaluating the ground truth annotations of the Image Subtask against the Video Subtask and vice versa

MediaEval 2016 - ININ Submission to Zero Cost ASR Task

multimediaeval

Presenter: Tejas Godambe ININ Submission to Zero Cost ASR Task at MediaEval 2016 In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Tejas Godambe, Naresh Kumar, Pavan Kumar, Veera Raghavendra, Aravind Ganapathiraju Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_31.pdf Video: https://youtu.be/e70xRjsUUts Abstract: This paper details the experiments conducted to train an as good performing Vietnamese speech recognition system as possible using public domain data only, as a part of the Zero Cost task at MediEval 2016. We explored techniques related to audio preprocessing, use of speaker’s pitch information, data perturbation, for building subspace Gaussian mixture acoustic model which is known for estimating robust parameters when the amount of data is less, and also unsupervised adaptation, RNN language model based lattice rescoring and system combination using ROVER technique.

MediaEval 2016 - IR Evaluation: Putting the User Back in the Loop

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MediaEval 2016 - UNIFESP Predicting Media Interestingness Task

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Presenter: Samuel G. Fadel UNIFESP at MediaEval 2016: Predicting Media Interestingness Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Jurandy Almeida Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_28.pdf Video: https://youtu.be/YLthKNczlcA Abstract: This paper describes the approach proposed by UNIFESP for the MediaEval 2016 Predicting Media Interestingness Task and for its video subtask only. The proposed approach is based on combining learning-to-rank algorithms for predicting the interestingness of videos by their visual content.

MediaEval 2016 - Simula Team @ Context of Experience Task

multimediaeval

Presenter: Konstantin Pogorelov Simula @ MediaEval 2016 Context of Experience Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Konstantin Pogorelov, Michael Riegler, Pål Halvorsen, Carsten Griwodz Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_53.pdf Video: https://youtu.be/FTIeGpHhURU Abstract: This paper presents our approach for the Context of Multimedia Experience Task of the MediaEval 2016 Benchmark. We present different analyses of the given data using different subsets of data sources and combinations of it. Our approach gives a baseline evaluation indicating that metadata approaches work well but that also visual features can provide useful information for the given problem to solve.

MediaEval 2016 - Approaches to and Issues Arising from Answering Natural Lang...

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Presenter: Donncha S. Ó Maidín OMDN at the C@merata 2016 Task: Approaches to and Issues Arising from Answering Natural Language Questions about Music Scores In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Donncha Ó. Maidín Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_58.pdf Video: https://youtu.be/9sTWdHdCP9c Abstract: The query is modeled as a parse tree where clauses from the query form children of the tree’s root. Children of each clause, in turn represent the query’s atomic units. A complete search involves visiting all score objects that are potential starting points for a match. Atomic units of the parse tree are compared, in sequence, with corresponding score objects. From each score object, appropriate paths are explored to find objects that match the sequence of tree’s atomic units. The parse tree is traversed in a depth first order. Iteration paths through the score follow either vertical or horizontal routes, the former for chord or harmonic interval comparisons, and the latter for melodic comparisons. Iteration between clauses results in the selection of new score staring points, appropriate to the temporal relationship between clauses. Results were limited on account of problems with importing some of the MusicXML files.

MediaEval 2016 - Multimodal Person Discovery in TV Broadcast

multimediaeval

Presenter: Hervé Bredin Multimodal Person Discovery in Broadcast TV at MediaEval 2016 In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Hervé Bredin, Claude Barras, Camille Guinaudeau Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_4.pdf Video: https://youtu.be/OOfOtjsmXV4 Abstract: We describe the “Multimodal Person Discovery in Broadcast TV” task of MediaEval 2016 benchmarking initiative. Participants are asked to return the names of people who can be both seen as well as heard in every shot of a collection of videos. The list of people is not known a priori and their names has to be discovered in an unsupervised way from media content using text overlay or speech transcripts for the primary runs. The task is evaluated using information retrieval metrics, based on a posteriori collaborative annotation of the test corpus.

Presenter: Miroslav Skácel BUT Zero-Cost Speech Recognition 2016 System Description In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Miroslav Skácel, Martin Karafiát, Lucas Ondel, Albert Uchytil, Igor Szöke Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_48.pdf Video: https://youtu.be/0pNiLLVTa28 Abstract: This paper describes our work on developing speech recognizers for Vietnamese. It focuses on procedures to prepare provided data precisely. We aim on analysis of the textual transcriptions in particular. Methods to filter out defective data to improve performance of final system are proposed and described in detail. We also propose cleaning of other textual data used for language modeling. Several architectures are investigated to reach both sub-tasks goals. The achieved results are discussed.

MediaEval 2016 - Emotional Impact of Movies Task

multimediaeval

Presenter: Emmanuel Dellandréa The MediaEval 2016 Emotional Impact of Movies Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Emmanuel Dellandréa, Liming Chen, Yoann Baveye, Mats Sjöberg, Christel Chamaret Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_6.pdf Video: https://youtu.be/c2itaauMVmQ Abstract: This paper provides a description of the MediaEval 2016 ”Emotional Impact of Movies” task. It continues builds on previous years’ editions of the Affect in Multimedia Task: Violent Scenes Detection. However, in this year’s task, participants are expected to create systems that automatically predict the emotional impact that video content will have on viewers, in terms of valence and arousal scores. Here we provide insights on the use case, task challenges, dataset and ground truth, task run requirements and evaluation metrics.

MediaEval 2016: A Multimodal System for the Verifying Multimedia Use Task

multimediaeval

Presenter: Maigrot Cédric MediaEval 2016: A Multimodal System for the Verifying Multimedia Use Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Cédric Maigrot, Vincent Claveau, Ewa Kijak, Ronan Sicre Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_45.pdf Video: https://youtu.be/ay1zWydnijY Abstract: This paper presents a multi-modal hoax detection system composed of text, source, and image analysis. As hoax can be very diverse, we want to analyze several modalities to better detect them. This system is applied in the context of the Verifying Multimedia Use task of MediaEval 2016. Experiments show the performance of each separated modality as well as their combination.

MediaEval 2015 - Privacy Protection Filter Using StegoScrambling in Video Sur...

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MediaEval 2016: A Hybrid Approach for Verifying Multimedia Use on Twitter

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Presenter: Quoc Tin Phan A Hybrid Approach for Multimedia Use Verification In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Quoc-Tin Phan, Alessandro Budroni, Cecilia Pasquini, Francesco G. B. De Natale Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_47.pdf Video: https://youtu.be/XypWh51QyMQ Abstract: Social networks enable multimedia sharing between worldwide users, however, there is no automatic mechanism implemented aiming to verifying multimedia use. This has been known as a highly challenging problem due to the variety of media types and huge amount of information they convey. As a participating team of MediaEval 2016, we propose a hybrid approach for detecting misused multimedia on Twitter which has been known as Verifying Multimedia Use task. Specifically, we designed a verfication system that can answer how likely an associated multimedia file is fake based on multiple forensic features and textual features, which were acquired by performing online text search and image reverse search. Next, effective post-based features and user-based features are utilized to validate the credibility of tweet posts. Finally, based on the assumption that a tweet sharing fake images or videos is likely to be fake, credibility scores of tweet posts and associated multimedia are fused to detect misused multimedia.

MediaEval 2016 - Verifying Multimedia Use Task Overview

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Presenters: Stuart E. Middleton and Christina Boididou Verifying Multimedia Use at MediaEval 2016 In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Christina Boididou, Symeon Papadopoulos, Duc-Tien Dang-Nguyen, Giulia Boato, Michael Riegler, Stuart E. Middleton, Andreas Petlund, and Yiannis Kompatsiaris Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_3.pdf Video: https://youtu.be/2Jx6OliFR-0 Abstract: This paper provides an overview of the Verifying Multimedia Use task that takes places as part of the 2016 MediaEval Benchmark. The task motivates the development of automated techniques for detecting manipulated and misleading use of web multimedia content. Splicing, tampering and reposting videos and images are examples of manipulation that are part of the task definition. For the 2016 edition of the task, a corpus of images/videos and their associated posts is made available, together with labels indicating the appearance of misuse (fake) or not (real) in each case as well as some useful post metadata.

MediaEval 2016 - Tag Propagation in Talking Face Graphs

multimediaeval

Presenter: Guillaume Gravier PUCMinas and IRISA at Multimodal Person Discovery In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Gabriel Sargent, Gabriel Barbosa de Fonseca, Izabela Lyon Freire, Ronan Sicre, Zenilton K. G. Patrocínio Jr, Silvio Jamil F. Guimarães, and Guillaume Gravier Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_34.pdf Video: https://youtu.be/O2Ecgy89hN4 Abstract: This paper describes the systems developed by PUC Minas and IRISA for the person discovery task at MediaEval 2016. We adopt a graph-based representation and investigate two tag-propagation approaches to associate overlays cooccurring with some speaking faces to other visually or audio-visually similar speaking faces. Given a video, we first build a graph from the detected speaking faces (nodes) and their audio-visual similarities (edges). Each node is associated to its co-occurring overlays (tags) when they exist. Then, we consider two tagpropagation approaches, respectively based on a random walk strategy and on Kruskal’s algorithm.

MediaEval 2016 - Placing Images with Refined Language Models and Similarity S...

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Presenter: Giorgos Kordopatis-Zilos Placing Images with Refined Language Models and Similarity Search with PCA-reduced VGG Features In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Giorgos Kordopatis-Zilos, Adrian Popescu, Symeon Papadopoulos, Yiannis Kompatsiaris Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_13.pdf Video: https://youtu.be/WR4I3CWjcR4 Abstract: We describe the participation of the CERTH/CEA-LIST team in the MediaEval 2016 Placing Task. We submitted five runs to the estimation-based sub-task: one based only on text by employing a Language Model-based approach with several refinements, one based on visual content, using geospatial clustering over the most visually similar images, and three based on a hybrid scheme exploiting both visual and textual cues from the multimedia items, trained on datasets of different size and origin. The best results were obtained by a hybrid approach trained with external training data and using two publicly available gazetteers.

MediaEval 2016 - UNED-UV @ Retrieving Diverse Social Images Task

multimediaeval

Ana Garcia Serrano UNED-UV @ Retrieving Diverse Social Images Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Angel C. González, Xaro B. Garcia, Ana García-Serrano, Esther de Ves Cuenca Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_17.pdf Video: https://youtu.be/EyuN7IA1HFk Abstract: This paper details the participation of the UNED-UV group at the MediaEval 2016 Retrieving Diverse Social Images Task using a multimodal approach. Several Local Logistic Regression models, which use the visual low-level features, estimate the relevance probability for all the images in the dataset. Then, the images are ranked by selecting the highest probability image at each of the textual clusters. These textual clusters are generated by making use of a textual algorithm based on Formal Concept Analysis (FCA) and Hierarchical Agglomerative Clustering (HAC) to detect the latent topics addressed. The images will be then diversified according to detected topics.

2016 MediaEval - Interestingness Task Overview

multimediaeval

Presenter: Claire-Hélène Demarty MediaEval 2016 Predicting Media Interestingness Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Claire-Hélène Demarty, Mats Sjöberg, Bogdan Ionescu, Thanh-Toan Do, Hanli Wang, Ngoc Q. K. Duong, and Frédéric Lefebvre Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_1.pdf Video: https://youtu.be/rAarQaEr9-w Abstract: This paper provides an overview of the Predicting Media Interestingness task that is organized as part of the MediaEval 2016 Benchmarking Initiative for Multimedia Evaluation. The task, which is running for the first year, expects participants to create systems that automatically select images and video segments that are considered to be the most interesting for a common viewer. In this paper, we present the task use case and challenges, the proposed data set and ground truth, the required participant runs and the evaluation metrics.

MediaEval 2016 - MLPBOON Predicting Media Interestingness System

multimediaeval

Presenter: Jayneel Parekh The MLPBOON Predicting Media Interestingness System for MediaEval 2016 In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Jayneel Parekh, Sanjeel Parekh Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_25.pdf Video: https://youtu.be/nAnrdYiy7nc Abstract: This paper describes the system developed by team MLPBOON for MediaEval 2016 Predicting Media Interestingness Image Subtask. After experimenting with various features and classifiers on the development dataset, our final system involves use of CNN features (fc7 layer of AlexNet) for the input representation and logistic regression as the classifier. For the proposed method, the MAP for the best run reaches a value of 0.229.

Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal...

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper62.pdf YouTube: https://youtu.be/gV-rvV3iFDA Pierre-Etienne Martin, Jenny Benois-Pineau, Boris Mansencal, Renaud Péteri and Julien Morlier : Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal CNN for MediaEval 2020. Proc. of MediaEval 2020, 14-15 December 2020, Online. This work presents a method for classifying table tennis strokes using spatio-temporal convolutional neural networks. The fine-grained classification is performed on trimmed video segments recorded at 120 fps with different players performing in natural conditions. From those segments, the frames are extracted, their optical flow is computed and the pose of the player is estimated. From the optical flow amplitude, a region of interest is inferred. A three stream spatio-temporal convolutional neural network using combination of those modalities and 3D attention mechanisms is presented in order to perform classification. Presented by: Pierre-Etienne Martin

HCMUS at MediaEval 2020: Ensembles of Temporal Deep Neural Networks for Table...

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper50.pdf Hai Nguyen-Truong, San Cao, N. A. Khoa Nguyen, Bang-Dang Pham, Hieu Dao, Minh-Quan Le, Hoang-Phuc Nguyen-Dinh, Hai-Dang Nguyen and Minh-Triet Tran : HCMUS at MediaEval 2020: Ensembles of Temporal Deep Neural Networks for Table Tennis Strokes Classification Task. Proc. of MediaEval 2020, 14-15 December 2020, Online. The Sports Video Classification Tasks in the Multimedia Evaluation 2020 Challenge focuses on classifying different types of table tennis strokes in video segments. In this task, we - the HCMUS Team - perform multiple experiments, which includes a combination of models such as SlowFast, Optical Flow, DensePose, R2+1, Channel-Separated Convolutional Networks, to classify 21 types of table tennis strokes from video segments. In total, we submit eight runs corresponding to five different models with different sets of hyper-parameters in each of our models. In addition, we apply some pre-processing techniques on the dataset in order for our model to learn and classify more accurately. According to the evaluation results, one of our team's methods out-performs the other team's. In particular, our best run achieves 31.35\% global accuracy, and all of our methods show potential results in terms of local and global accuracy for action recognition tasks.

Sports Video Classification: Classification of Strokes in Table Tennis for Me...

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper2.pdf YouTube: https://youtu.be/-bRL868b8ys Pierre-Etienne Martin, Jenny Benois-Pineau, Boris Mansencal, Renaud Péteri, Laurent Mascarilla, Jordan Calandre and Julien Morlier : Sports Video Classification: Classification of Strokes in Table Tennis for MediaEval 2020. Proc. of MediaEval 2020, 14-15 December 2020, Online. Fine-grained action classification has raised new challenges compared to classical action classification problems. Sport video analysis is a very popular research topic, due to the variety of application areas, ranging from multimedia intelligent devices with user-tailored digests, up to analysis of athletes' performances. Running since 2019 as a part of MediaEval, we offer a task which consists in classifying table tennis strokes from videos recorded in natural conditions at the University of Bordeaux. The aim is to build tools for teachers, coaches and players to analyse table tennis games. Such tools could lead to an automatic profiling of the player and adaptation of his training for improving his/her sport skills more efficiently. Presented by: Pierre-Etienne Martin

Predicting Media Memorability from a Multimodal Late Fusion of Self-Attention...

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper61.pdf YouTube: https://youtu.be/brmI4g3jLS4 Ricardo Kleinlein, Cristina Luna-Jiménez, Fernando Fernández-Martínez and Zoraida Callejas : Predicting Media Memorability from a Multimodal Late Fusion of Self-Attention and LSTM Models. Proc. of MediaEval 2020, 14-15 December 2020, Online. This paper reports on the GTH-UPM team experience in the Predicting Media Memorability task at MediaEval 2020. Teams were requested to predict memorability scores at both short-term and long-term, understanding such score as a measure of whether a video was perdurable in a viewer's memory or not. Our proposed system relies on a late fusion of the scores predicted by three sequential models, each trained over a different modality: video captions, aural embeddings and visual optical flow-based vectors. Whereas single-modality models show a low or zero Spearman correlation coefficient value, their combination considerably boosts performance over development data up to 0.2 in the short-term memorability prediction subtask and 0.19 in the long-term subtask. However, performance over test data drops to 0.016 and -0.041, respectively. Presented by: Ricardo Kleinlein

Essex-NLIP at MediaEval Predicting Media Memorability 2020 Task

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper52.pdf Janadhip Jacutprakart, Rukiye Savran Kiziltepe, John Q. Gan, Giorgos Papanastasiou and Alba G. Seco de Herrera : Essex-NLIP at MediaEval Predicting Media Memorability 2020 Task. Proc. of MediaEval 2020, 14-15 December 2020, Online. In this paper, we present the methods of approach and the main results from the Essex NLIP Team’s participation in the MediEval 2020 Predicting Media Memorability task. The task requires participants to build systems that can predict short-term and long-term memorability scores on real-world video samples provided. The focus of our approach is on the use of colour-based visual features as well as the use of the video annotation meta-data. In addition, hyper-parameter tuning was explored. Besides the simplicity of the methodology, our approach achieves competitive results. We investigated the use of different visual features. We assessed the performance of memorability scores through various regression models where Random Forest regression is our final model, to predict the memorability of videos.

Overview of MediaEval 2020 Predicting Media Memorability task: What Makes a V...

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper6.pdf YouTube: https://youtu.be/ySGGu_4vaxs Alba García Seco De Herrera, Rukiye Savran Kiziltepe, Jon Chamberlain, Mihai Gabriel Constantin, Claire-Hélène Demarty, Faiyaz Doctor, Bogdan Ionescu and Alan F. Smeaton : Overview of MediaEval 2020 Predicting Media Memorability task: What Makes a Video Memorable? Proc. of MediaEval 2020, 14-15 December 2020, Online. This paper describes the MediaEval 2020 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 previous editions. This year the videos are a subset of the TRECVid 2019 Video to Text dataset, containing more action rich video content as compare with the 2019 task. In this paper a description of some aspects of this task is provided, including its main characteristics, a description of the collection, the ground truth dataset, evaluation metrics and the requirements for the run submission. Presented by: Rukiye Savran Kiziltepe

Fooling an Automatic Image Quality Estimator

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper45.pdf Benoit Bonnet, Teddy Furon and Patrick Bas : Fooling an Automatic Image Quality Estimator. Proc. of MediaEval 2020, 14-15 December 2020, Online. In this paper we present our work on the 2020 MediaEval task: Pixel "Privacy: Quality Camouflage for Social Images". Blind Image Quality Assessment (BIQA) is a classifier that for any given image will return a quality score. Our task is to modify an image to decrease its BIQA score while maintaining a good perceived quality. Since BIQA is a deep neural network, we worked on an adversarial attack approach of the problem.

Fooling Blind Image Quality Assessment by Optimizing a Human-Understandable C...

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper16.pdf YouTube: https://youtu.be/ix_b9K7j72w Zhengyu Zhao : Fooling Blind Image Quality Assessment by Optimizing a Human-Understandable Color Filter. Proc. of MediaEval 2020, 14-15 December 2020, Online. This paper presents the submission of our RU-DS team to the Pixel Privacy Task 2020. We propose to fool the blind image quality assessment model by transforming images based on optimizing a human-understandable color filter. In contrast to the common work that relies on small, $L_p$-bounded additive pixel perturbations, our approach yields large yet smooth perturbations. Experimental results demonstrate that in the specific context of this task, our approach is able to achieve strong adversarial effects, but has to sacrifice the image appeal. Presented by: Zhengyu Zhao

Pixel Privacy: Quality Camouflage for Social Images

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper77.pdf YouTube: https://youtu.be/8Rr4KknGSac Zhuoran Liu, Zhengyu Zhao, Martha Larson and Laurent Amsaleg : Pixel Privacy: Quality Camouflage for Social Images. Proc. of MediaEval 2020, 14-15 December 2020, Online. High-quality social images shared online can be misappropriated for unauthorized goals, where the quality filtering step is commonly carried out by automatic Blind Image Quality Assessment (BIQA) algorithms. Pixel Privacy benchmarks privacy-protective approaches that protect privacy-sensitive images against unethical computer vision algorithms. In the 2020 task, participants are encouraged to develop camouflage methods that can effectively decrease the BIQA quality score of high-quality images and maintain image appeal. The camouflaged images need to be either imperceptible to the human eye, or it can be a visible enhancement. Presented by: Zhuoran Liu

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Paper: http://ceur-ws.org/Vol-2882/paper73.pdf YouTube: https://youtu.be/TadJ6y7xZeA Thuc Nguyen-Quang, Tuan-Duy Nguyen, Thang-Long Nguyen-Ho, Anh-Kiet Duong, Xuan-Nhat Hoang, Vinh-Thuyen Nguyen-Truong, Hai-Dang Nguyen and Minh-Triet Tran : HCMUS at MediaEval 2020:Image-Text Fusion for Automatic News-Images Re-Matching. Proc. of MediaEval 2020, 14-15 December 2020, Online. Matching text and images based on their semantics has an important role in cross-media retrieval. However, text and images in articles have a complex connection. In the context of MediaEval 2020 Challenge, we propose three multi-modal methods for mapping text and images of news articles to the shared space in order to perform efficient cross-retrieval. Our methods show systemic improvement and validate our hypotheses, while the best-performed method reaches a recall@100 score of 0.2064. Presented by: Thuc Nguyen-Quang

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Paper: http://ceur-ws.org/Vol-2882/paper47.pdf YouTube: https://youtu.be/vMsM4zg2-JY Tien-Phat Nguyen, Tan-Cong Nguyen, Gia-Han Diep, Minh-Quan Le, Hoang-Phuc Nguyen-Dinh, Hai-Dang Nguyen and Minh-Triet Tran : HCMUS at Medico Automatic Polyp Segmentation Task 2020: PraNet and ResUnet++ for Polyps Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online. The Medico task, MediaEval 2020, explores the challenge of building accurate and high-performance algorithms to detect all types of polyps in endoscopic images. We proposed different approaches leveraging the advantages of either ResUnet++ or PraNet model to efficiently segment polyps in colonoscopy images, with modifications on the network structure, parameters, and training strategies to tackle various observed characteristics of the given dataset. Our methods outperform the other teams' methods, for both accuracy and efficiency. After the evaluation, we are at top 2 for task 1 (with Jaccard index of 0.777, best Precision and Accuracy scores) and top 1 for task 2 (with 67.52 FPS and Jaccard index of 0.658).

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MediaEval 2016 - Emotional Impact of Movies Task

1. The MediaEval 2016 Emotional Impact of Movies Task Run submissions • Up to 5 runs for each subtask • A required run which uses no external training data, only the provided development data is allowed Evaluation Metrics: • Mean Square Error • Pearson’s Correlation Coefficient Development dataset: LIRIS-ACCEDE Discrete LIRIS-ACCEDE • 9800 video clips from 160 movies under Creative Commons licenses • Duration between 8s and 12s • Cross-validated through a controlled experimental protocol Continuous LIRIS-ACCEDE • 30 movies • Duration between 117s and 4,566s (total duration: ~7 hours) • Continuous induced valence and arousal self-assessments Test dataset: • From 49 new movies under Creative Commons licenses • 1,200 additional short video clips for the first subtask (between 8 and 12 seconds) • 10 additional long movies (from 25 minutes to 1 hour and 35 minutes) for the second subtask (for a total duration of 11.48 hours) Sqdf sdf Ground truth Valence and arousal ranking: • Pairwise video comparisons on CrowdFlower • Annotators asked to focus on the emotion they felt • Simple task: • Which one conveys the most positive emotion? • Which one conveys the calmest emotion? From rankings to ratings: • Ratings collected for 40 video clips regularly distributed • 28 participants • Ratings estimated using Gaussian Process models Continuous annotation: • Induced valence and arousal self-assessments • 16 participants • Modified Gtrace interface and joystick Task Description • Deploy multimedia features and models to automatically predict the emotional impact of movies • Emotion considered in terms of induced valence and arousal Two subtasks: • Global emotion prediction: given a short video clip (around 10 seconds), participants’ systems are expected to predict a score of induced valence (negative-positive) and induced arousal (calm-excited) for the whole clip; • Continuous emotion prediction: as an emotion felt during a scene may be influenced by the emotions felt during the previous ones, the purpose here is to consider longer videos, and to predict the valence and arousal continuously along the video. Thus, a score of induced valence and arousal should be provided for each 1s- segment of the video. Context • An evolution of previous years tasks on violence and affect prediction from videos • Applications: • Personalized content delivery • Movie recommendation • Video editing supervision • Video summarization • Protection of children from potential harmful content Organizers: Emmanuel Dellandréa, Liming Chen, Yoann Baveye, Mats Sjöberg, Christel Chamaret Contact: Emmanuel Dellandréa – emmanuel.dellandrea@ec-lyon.fr Representation of emotions Credits and license information is available here: http://liris-accede.ec-lyon.fr/database.php Arousal Valence

MediaEval 2016 - Emotional Impact of Movies Task

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MediaEval 2016 - Emotional Impact of Movies Task