MediaEval Philosophy

MediaEval is a benchmark and provides standardized task descriptions, data sets, and evaluation procedures. Benchmarks make possible systematic comparison of the performance of different approaches to problems of retrieving or analyzing multimedia content. MediaEval promotes the benefits that arise from methodical comparison: identification of state-of-the-art algorithms and promotion of research progress.

MediaEval pursues its objective of offering a benchmark to the multimedia research community within a broader philosophy that emphasizes understanding. Specifically, we seek to transcend conventional benchmarks by striving to gain qualitative insight about our data, our evaluation metrics, our ground truth or evaluation procedure, and the fundamental nature of the challenges that our tasks represent.

Targeting such insight helps us to avoid devoting a disproportionate amount of attention to tasks for which performance is easy or obvious to measure using conventional quantitative metrics. It also encourages the researchers who participate in the benchmark to take leaps of innovation that may not pay off in the short run, but are promising in the long run. We try to identify and appreciate the value of innovative ideas before they are well developed enough to improve metrics and to prevent promising, but still immature, approaches from being abandoned prematurely.

MediaEval expresses the importantance that it places on understanding with MediaEval Distinctive Mentions (MDMs). Each year, the organizers of the tasks single out participating team that have gone beyond solving the task to making a further contribution. Recipients of MDMs are teams that stand out because their approach to the MediaEval task was highly innovative and/or delivered important insight with potential for the future. Teams that receive MDMs are not necessarily those teams that scored at the top of the task ranking/leaderboard.

From 2013-2019, MDMs were informal and announced at the workshop but not otherwise publicized. Starting 2020, we are publishing the MDMs online (see list below). (Note: The task rankings can be found in the workshop overview presentations of the individual tasks.)

In 2022, MediaEval introduced the Quest for Insight, which encourages explicit discussion of how participants can explore tasks in a way that goes beyond exclusive focus on the evaluation metric. We hope that the Quest for Insight will lead to many inspiring MDMs moving forward.

MediaEval Distinctive Mentions (MDMs)

These are the distinctive mentions awarded at MediaEval.

2026

The papers for 2026 are not yet officially published. We will update the links to the papers once they are updated.

To NTHU-CY team: Hao-Tien Yu and Yi-En Dong

For: Their rigorous Insight into EEG subject-variability biases and their bold Risk-taking in proving that generic video aesthetics do not drive commercial memorability.
From: The organizers of Memorability

To CSAIL-TCNJ team: Olufela Fieulleteau, Conner Perriello, Sejong Yoon

For: Their comprehensive study of the effect of different modalities and concepts on memorability.
From: The organizers of Memorability

To AIT-SoftBank - Hengwei Zhao & Takayuki Hori

For: Elaborately evaluating, finetuning, and combining VLMs, resulting in very good generalizability
From: The organizers of Synthetic Images

To ANLAM-NET-M - Berkay Bayramoglu, Ismail Erol, Rukiye Savran Kiziltepe & Murat Karakus

For: A novel fusion of complimentary forensic features
From: The organizers of Synthetic Images

To LaSID–UFSCar

For: Going against the grain and finding that the best performing models do not need to be specialist models.
From: The organizers of Medico

To CERTH-ITI - Damianos Galanopoulos, Vasileios Mezaris

For: Their Actor-Critic Image Generation approach for producing, evaluating, and refining image recommendation.
From: The organizers of NewsImages

To Jarno van Arkel

For: A quest-for-insight paper and presentation that gave the most clear-eyed, comprehensive account of annotator disagreement in the task.
From: The organizers of Missing Pieces and Misinformation

To Moez Ur Rehman - FAST-NU

For: The best-performing system, a distinctive approach, and failure analysis that genuinely illuminated the task.
From: The organizers of Missing Pieces

To Missing Pieces Task: Identifying social media posts with implicit messages

For: An interesting, timely, and successful new task, at the intersection of linguistics, disinformation and AI.
From: The MediaEval Coordination Committee

To IIS ZHAW - Kimia Rabishokr

For: An excellent contribution to multimodal city-level summarisation
From: The organizers of MultiSumm

2025

The papers for 2025 are not yet officially published (we are sorry for the delay). We will update the links to the papers once they are updated.

To Team SSN-CSE-CODECATALYSTS

For: An original approach for synthetic image detection, and participating in both subtasks.
From: The organizers of Synthetic Images: Advancing detection of generative AI used in real-world online images

To Aashutosh Ganesh, Iris Huijben, Bulat Khaertdinov, Iskaj Janssen, Mirela Popa and Nava Tintarev

For: In-depth analysis of the data and task, as well as an original and reflective approach to the task
From: The organizers of Memorability: Predicting movie and commercial memorability

To Itbaan Safwan, Muhammad Annas Shaikh, Muhammad Haaris, Ramail Khan and Muhammad Atif

For: For demonstrating that everywhere is somewhere, and their model knew where. “Everywhere’s been where it is ever since it was first put there. It’s called geography.” - Terry Pratchett
From: The organizers of Medico 2025: Visual Question Answering (with multimodal explanations) for Gastrointestinal Imaging

To Xiaomeng Wang and Bram Bakker

For: The analysis of article-specific image retrieval and generation strategies
From: The organizers of NewsImages – Retrieval and generative AI for news thumbnails

To Lucien Heitz

For: Leadership in the NewsImage Task. Stepping up to the plate when needed.
From: The MediaEval Coordinators

To Anastasia Potyagalova

For: Pioneering a new task in summarization (and elegantly handling randomly flipping slides during the task overview presentation.)
From: The MediaEval Coordinators

2023

To Team SELAB-HCMUS

For: Properly evaluating and addressing the data imbalance problem
From: The organizers of Medical Multimedia Task - Transparent Tracking of Spermatozoa
Paper: Optimizing Sperm Detection and Tracking in Fluids with Equalize Class Representation Augmentation

To Sci-LAB, Sensory-Cognitive Interaction Lab, Stockholm University

For: Participating in all four subtasks, obtaining the best results in all, and reporting their work in detail
From: The organizers of MUSTI - Multimodal Understanding of Smells in Texts and Images with Zero-shot Evaluation
Paper: Enhancing Multimodal Language Models with Olfactory Information

To Lucien Heitz, Abraham Bernstein, and Luca Rossetto

For: Giving new insights into user preferences based on empirical studies analyzing diverse matching strategies.
From: The organizers of NewsImages
Paper: An Empirical Exploration of Perceived Similarity between News Article Texts and Images

To Bhuvana Jayaraman, Mirnalinee Tt, Harshida Sujatha Palaniraj, Mohith Adluru and Sanjjit Sounderrajan Nadar College of Engineering, India

For: Providing an efficient solution to text extraction in swimming scoreboards images
From: The organizers of SportsVideo
Paper: Automated Recognition of Sports Scores using PyTessaract OCR and CNN: SportsVideo Task at MediaEval 2023

To Team FAST-NUCES-KHI: Muhammad Mustafa Ali Usmani, Humna Faisal and Muhammad Atif Tahir

For: Their interesting analysis of the differences between our datasets and the problems these differences pose
From: The organizers of Memorability
Paper: The Impact of Transformers Ensemble on Model Memorability and Generalizability

2022

To Jane Arleth Dela Cruz and the InDeep-RU team (Radboud University)

For: The InDepth investigation of the human annotation that was used as ground truth for the evaluation
From: The organizers of the Multimedia Analysis of Disaster-Related Social Media Data (DisasterMM) task
Paper: Understanding Fine-tuned BERT Models for Flood Location Extraction on Twitter Data

To Damir Korenčić, Ivan Grubišić, Gretel Liz De La Peña Sarracén, Alejandro Hector Toselli, Berta Chulvi and Paolo Rosso (Universitat Politècnica de València and Ruđer Bošković Institute, Zagreb)

For: Investigating the use of large language models in addition to providing a strong solution in the challenge
From: The organizers of the Fake News Detection task
Paper: Tackling Covid-19 Conspiracies on Twitter using BERT Ensembles, GPT-3 Augmentation, and Graph NNs

To Yi Shao, Yang Zhang, Wenbo Wan, Jing Li and Jiande Sun, Shandong Normal University (SDNU), China

For: Being able to see and read smells, coping with a novel task and coming up with an innovative solution
From: The organizers of the Multimodal Understanding of Smells in Texts and Images (MUSTI) Task
Paper: CLIP Pre-trained Models for Cross-modal Retrieval in NewsImages 2022

To Team HCMUS, University of Science, VNU-HCM, Viet Nam National University Ho Chi Minh City, John von Neumann Institute, VNU-HCM, Vietnam

For: A novel tail-aware sperm detection method and deep evaluations using five different deep learning models
From: The organizers of the Medico Medical Multimedia task
Paper: Tail-Aware Sperm Analysis for Transparent Tracking of Spermatozoa

To Team Erectus, Intellectus Inc., Poznan University of Technology, University of Warsaw

For: A novel method introduced for predicting motility level of sperm samples (subtask 2) and participating in the subtask 3
From: The organizers of the Medico Medical Multimedia task
Paper:

To Damianos Galanopoulos and Vasileios Mezaris from ITI-CERTH, Greece

For: The deep investigation of the algorithms parameters and the influence of the provided dataset fields
From:The organizers of the NewsImages task
Paper: Cross-modal Networks and Dual Softmax Operation for MediaEval NewsImages 2022

To Mirko Agarla, Luigi Celona and Raimondo Schettini from University of Milano-Bicocca

For: Great coverage of the first two tasks and great results overall
From: The organizers of the task Predicting Multimedia Memorability
Paper: Predicting Video Memorability Using a Model Pretrained with Natural Language Supervision

To Finn Bartels and Leonard Hacker from the University of Leipzig, Germany

For: Their risk-taking and ablation study via the different combinations and fusion of modalities.
From: The organizers of the Sports Video task
Paper: Fine-Grained Action Detection with RGB and Pose Information using Two Stream Convolutional Networks

To Khanh-Linh Vo, Gia-Nghi Phuc-Nguyen, Tuong-Nghiem Diep, and Nhat-Hao Pham from VNU-HCMUS

For: Address the correlation of data modalities in forecasting long-term AQI and get insights into people’s experience and the model’s knowledge
From: The organizers of Urban Air the Urban Life and Air Pollution task
Paper: Forecasting Long-Term Urban Air Quality Index using Multi-Model

To Team DCU-Insight-AQ from Dublin City University and University College Dublin, Ireland

For: Address the problems of significant data gaps, spatial information, and optimal data modality combination
From: The organizers of Urban Air the Urban Life and Air Pollution task
Paper: Managing Large Dataset Gaps in Urban Air Quality Prediction: DCU-Insight-AQ at MediaEval 2022

To Birk Torpmann-Hagen from SimulaMet, Norway

For: Providing insight into the Njord dataset that can also be generalized to machine learning datasets as a whole
From: The organizers of NjordVid: A Fishing Trawler Video Analytics Task
Paper: Addressing Generalization Failure in Deep Detection Models for Fishing Trawler Video Analytics

To Tor-Arne Schmidt Nordmo, UiT, Norway and the NjordVid team

For: For daring and dedication in organizing a new video analysis and privacy task to continue an important tradition at MediaEval
From: The MediaEval 2022 Benchmark Coordinating Committee

To The MUSTI organization team

For: For opening the eyes (and noses) of MediaEval participants to the modality of smell with the MUSTI task on Multimodal Understanding of Smells in Texts and Images
From: The MediaEval 2022 Benchmark Coordinating Committee

To The Memorability Task

For: For their impressive contribution to promoting insight into data and approaches at MediaEval in the first year of Quest for Insight papers
From: The MediaEval 2022 Benchmark Coordinating Committee

2021

To Omar Meriwani (Real Sciences)

For: For his creativity on taking one step-forward using the task data and exploring other approaches
From: The organizers of Emerging News task
Paper: Mediaeval 2021 Emerging News: Detection of Emerging News from Live News Stream Based on Categorization of News Annotations

To Hao Hao Tan (team Mirable)

For: Showing the value of additional input representations describing tonality
From: The Organizers of Emotions and Themes in Music Task
Paper: Semi-Supervised Music Emotion Recognition using Noisy Student Training and Harmonic Pitch Class Profiles

To Cláudio, Rui, and David (Team NewsSeek-NOVA)

For: Teaching a Transformer to Recognize Faces
From: The Organizers of the News Images Task
Paper: NewsSeek-NOVA at MediaEval 2021: Context-enriched Multimodal Transformers For News Images Re-matching

To SELAB-HCMUS

For: Amount of effort and diverse out-of box approaches is testament to the fact that they achieved best results
From: Organizers of the task: Visual Sentiment Analysis: A Natural Disaster Use-case
Paper: HCMUS at MediaEval 2021: Efficient methods of Metadata Embedding and Augmentation for Visual Sentiment Analysis

To Youri Peskine, Giulio Alfarano, Ismail Harrando, Paolo Papotti and Raphaël Troncy (team D2KLab)

For: For the best task results achieved via effective ensembling a swarm of transformer-based models
From: Organizers of the task: FakeNews: Corona Virus and Conspiracies Multimedia Analysis Task
Paper: Detecting COVID-19-Related Conspiracy Theories in Tweets

To Thomas Girault, Cheikh Brahim El Vaigh, Cyrielle Mallart and Duc Hau Nguyen (team Deltamap)

For: For the successful use of the prompt-based learning NLP paradigm on top of toxicity trained-model
From: Organizers of the task: FakeNews: Corona Virus and Conspiracies Multimedia Analysis Task
Paper: Detecting Fake News Conspiracies with Multitask and Prompt-Based Learning

To Zeynep Pehlivan (team FakeINA)

For: For the fresh look at the task and successful solving pure NLP task in GNN domain
From: Organizers of the task: FakeNews: Corona Virus and Conspiracies Multimedia Analysis Task
Paper: On The Pursuit of Fake News : Graph Neural Network meets NLP

To Muhammad Asif Ayub, Khubaib Ahmad, Kashif Ahmad, Nasir Ahmad, and Ala Al-Fuqaha (CSEI team)

For: Their idea to translate Italian tweets with positive label to English and then back to Italian to increase and balance the dataset
From: Organizers of the task WaterMM: Water Quality in Social Multimedia
Paper: Deep Models for Visual Sentiment Analysis of Disaster-related Multimedia Content

To Yijun Qian, Lijun Yu, Wenhe Liu and Alexander Hauptmann

For: Their ablation method, quality paper and top rank in the task
From: Organizers of the Sports Video task
Paper: Learning Unbiased Transformer for Long-Tail Sports Action Classification

To Alison Reboud, Ismail Harrando, Jorma Laaksonen and Raphaël Troncy

For: For exploring interesting concepts such as perplexity and achieving top scores in a number of subtasks.
From: Organizers of the task: Predicting Media Memorability
Paper: Exploring Multimodality, Perplexity and Explainability for Memorability Prediction

To Ali Akbar, Muhammad Atif Tahir, Muhammad Rafi

For: Their detailed preprocessing and validation steps, taking risk to attempt all three subtasks, and top ranking in the task.
From: Organizers of the task: Insight for Wellbeing: Cross-Data Analytics for (transboundary) Haze Prediction
Paper: Towards Time Series Forecasting of Cross-Data Analytics for Haze Prediction

To Andrea Storås (SimulaMet)

For: For her unique submission on generating segmentation masks unsupervised.
From: Organizers of the task: Medico: Transparency in Medical Image Segmentation
Paper: Unsupervised Image Segmentation via Self-Supervised Learning Image Classification

To Felicia Ly Jacobsen (University of Oslo)

For: For making her submission more transparent by measuring uncertainty in the predicted segmentation masks.
From: Organizers of the task: Medico: Transparency in Medical Image Segmentation
Paper: Predictive Uncertainty Masks from Deep Ensembles in Automated Polyp Segmentation

2020

To SAIL-MiM-USC

For: Using different loss functions to overcome the dataset imbalance and showing importance of optimization.
From: Emotions and Themes in Music Task organizers
Paper: MediaEval 2020 Emotion and Theme Recognition in Music Task: Loss Function Approaches for Multi-label Music Tagging
Presentation: SlideShare link
Video: YouTube link

To Linkmedia

For: Their overall performance in both text and graph classification by using artificial examples and user reputation.
From: FakeNews Task organizers
Paper: Detecting Fake News in Tweets from Text and Propagation Graph: IRISA’s Participation to the FakeNews Task at MediaEval 2020
Video: YouTube link

To MG-UCB

For: Their creative graph sampling approach exploring retweet cascade properties in small subgraphs.
From: FakeNews Task organizers
Paper: Detecting Conspiracy Theories from Tweets: Textual and Structural Approaches

To DL-TXST

For: Their implementation of auxiliary optical character recognition for text classification and creation of website with an overview of task-related information and resources.
From: FakeNews Task organizers
Paper: Enriching Content Analysis of Tweets Using Community Discovery Graph Analysis
Video: YouTube link

To FIG

For: For using only textual information and for tackling class imbalance with random undersampling instead of oversampling.
From: Flood-related Multimedia organizers
Paper: A Tweet Text Binary Artificial Neural Network Classifier

To FAST-NU-DS

For: For using fused textual and visual information and for ensembling different classifiers.
From: Flood-related Multimedia organizers
Paper: An Ensemble Based Method for the Classification of Flooding Event Using Social Media Data
Presentation: SlideShare link
Video: YouTube link

To AISIA

For: Their overall scores and the best utilization of lifelog data characteristics.
From: Insight for Wellbeing Task organizers
Paper: A2QI: An Approach for Air Pollution Estimation in MediaEval 2020
Presentation: SlideShare link
Video: YouTube link

To QHL-UIT

For: Their simple but efficient approach for capturing spatio-temporal-concept correlation of cross-data.
From: Insight for Wellbeing Task organizers
Paper: Insights for Wellbeing: Predicting Personal Air Quality Index Using Regression Approach

To AI-JMU

For: Not only showing where their method succeeded, but also showing where it failed.
From: Medico Task Organizers
Paper: Bigger Networks are not Always Better: Deep Convolutional Neural Networks for Automated Polyp Segmentation

To All Medico Participants

For: Making this the biggest year for Medico despite this year’s challenges and it being online.
From: Medico Task Organizers

To DCU-Audio

For: Their overall performance and the use of Memento dataset as external data.
From: Predicting Media Memorability Task organisers
Paper: Leveraging Audio Gestalt to Predict Media Memorability
Video: YouTube link

To MG-UCB

For: Their good results and their use of multiple sources (audio, text, visual) in their approach.
From: Predicting Media Memorability Task organisers
Paper: Multi-Modal Ensemble Models for Predicting Video Memorability
Video: YouTube link

To HCMUS_Team

For: Their novel approach to achieving both the adversarial effects and image enhancement in an end-to-end manner by optimizing an image-to-image translation model.
From: Pixel Privacy Task Organizers
Paper: HCMUS at Pixel Privacy 2020: Quality Camouflage with Back Propagation and Image Enhancement

To Linkmedia

For: Their novel attempt to specifically address the JPEG compression by quantization in the DCT domain.
From: Pixel Privacy Task Organizers
Paper: Fooling an Automatic Image Quality Estimator

To iCV-UT

For: Their proposed cascade method based on stroke decomposition for their classification.
From: Sports Video Classification Task organizers
Paper: Spatio-Temporal Based Table Tennis Hit Assessment Using LSTM Algorithm

To Toyohashi University of Technology

For: Their ablation study on stroke classification using different sources of information.
From: Sports Video Classification Task organizers
Paper: Leveraging Human Pose Estimation Model for Stroke Classification in Table Tennis