Update memorability.md

Author: cmihaigabriel

_editions/2025/tasks/memorability.md

@@ -21,7 +21,7 @@ The aim of this task is to predict how memorable a piece of media (e.g., movie e
 * _Challenge 1.1: How memorable is this video (movie excerpts)?_ - Video-based prediction: The goal of this task is to predict how memorable a video is based on movie excerpts. Participants are expected to develop automatic systems that predict the memorability scores of new videos. The memorability score indicates the probability of a video being remembered by viewers. To achieve this, participants will use a subset of the Movie Memorability dataset, which includes videos, their corresponding memorability scores.  Participants are free to use only the modalities relevant to their approach, enabling a broad range of methodologies.
 * _Challenge 1.2: Is this person familiar with this video?_ - EEG-based detection of recall: This task requires participants to automatically detect whether a person is remembering a video from a movie they previously watched. To do this, participants may use only features extracted from the EEG data, without using any features from the videos themselves.
 
-**Subtask 2: Commercial/Ad Memorability.** This task evaluates long-term memory performance in recognizing commercial videos. Participants will use the Commercial Video dataset, which contains commercial videos along with their memorability and brand memorability scores, to train their systems. The trained models will then predict the scores for new, unseen commercial videos (product, brand, and concept presentations and discussions). This challenge does not include EEG data.
+**Subtask 2: Commercial/Ad Memorability.** This task evaluates long-term memory performance in recognizing commercial videos. Participants will use the VIDEM dataset, which contains commercial videos along with their memorability and brand memorability scores, to train their systems. The trained models will then predict the scores for new, unseen commercial videos (product, brand, and concept presentations and discussions). This challenge does not include EEG data.
 * _Challenge 2.1: How memorable is this commercial video?_ - Video-based prediction: Like in challenge 1.1, the goal of this task is to predict how memorable a commercial video is. Therefore, participants are expected to develop automatic systems that predict the memorability scores of commercial videos. The memorability score indicates the probability of a commercial video being remembered by viewers.
 * _Challenge 2.2: Can you predict the brand memorability?_ - Video-based prediction: The goal of this task is to predict the brand memorability associated with a commercial video. Participants are expected to develop automatic systems that can predict the brand memorability score based on the content of the commercial video. This score indicates the probability of a commercial video brand being remembered by viewers. 
 
@@ -34,7 +34,7 @@ This task aims to develop models that predict the memorability of multimedia con
 This year’s task extends the state of the art by focusing on the memorability of multimedia content within the specific domains of movies and commercials. While previous research has explored the general memorability of videos and images, there has been limited focus on how this concept applies to the nuanced structure of films and advertisements. By addressing this gap, we aim to deepen our understanding of how human cognition interacts with multimedia, providing valuable insights into what makes content memorable and how it can be optimized for various applications across different industries, including both commercial and non-commercial use cases.
 
 _New for 2025._ 
-In 2025, the MediaEval Media Memorability Task introduces two new datasets: the Movie Memorability dataset and the Commercial Video dataset. These additions offer exciting opportunities for participants to explore the memorability of movie excerpts and commercial videos across various real-world contexts. This year, the task continues to build on past efforts by integrating multimodal data, including video content, memorability scores, and EEG data collected during memorability experiments, while encouraging innovative approaches to improve prediction accuracy. Additionally, a new challenge is introduced, focusing on brand memorability prediction. In this challenge, participants are not tasked with predicting the memorability of videos but with predicting a brand memorability score for commercial videos. This new challenge seeks to deepen our understanding of how brands are remembered within multimedia content, adding an intriguing layer of complexity to the task.
+In 2025, the MediaEval Media Memorability Task introduces two new datasets: the Movie Memorability dataset and the VIDEM dataset. These additions offer exciting opportunities for participants to explore the memorability of movie excerpts and commercial videos across various real-world contexts. This year, the task continues to build on past efforts by integrating multimodal data, including video content, memorability scores, and EEG data collected during memorability experiments, while encouraging innovative approaches to improve prediction accuracy. Additionally, a new challenge is introduced, focusing on brand memorability prediction. In this challenge, participants are not tasked with predicting the memorability of videos but with predicting a brand memorability score for commercial videos. This new challenge seeks to deepen our understanding of how brands are remembered within multimedia content, adding an intriguing layer of complexity to the task.
 
 #### Target group
 
@@ -48,7 +48,7 @@ One dataset will be provided for each subtask.
 
 For subtask 1, a subset of the [Movie Memorability dataset](https://www.interdigital.com/data_sets/movie-memorability-dataset) will be used. This is a collection of movie excerpts and corresponding ground-truth files based on the measurement of long-term memory performance when recognizing small movie excerpts from weeks to years after having viewed them. It is accompanied with audio and video features extracted from the movie excerpts. EEG data recorded while viewing this subset will be also provided. EEG data were recorded while 27 participants viewed a subset of clips from the dataset. The clips were selected to include both previously seen and unseen movies. After viewing each clip, participants were asked if they remembered seeing it before. In total 3484 epochs of 64 channel EEG data are available, of which 2122 were not recognised and 1362 were remembered. 
 
-For subtask 2, the Commercial Video dataset will be used. It focuses on video and brand memorability in commercial advertisements, including some educational or explanatory videos. Developed through a university-business collaboration between the University of Essex and Hub, with support from Innovate UK’s Knowledge Transfer Partnership (grant agreement No. 11071), This is  a collection of commercial advertisements and corresponding ground-truth files based on the measurement of long-term memory performance when recognizing them from 24 to 72 hours after having viewed them.  Each video is accompanied with metadata such as titles, descriptions, number of views, and duration and audio and video features extracted from the commercial advertisements. The dataset consists of 429 commercial videos sampled from a larger collection of 4,791 videos published on YouTube between June 2018 and June 2021. Video lengths range from 7 seconds to 94 minutes. For longer videos, users are allowed to watch only 1 minute. 
+For subtask 2, the VIDEM (VIDeo Effectiveness and Memorability) dataset will be used. It focuses on video and brand memorability in commercial advertisements, including some educational or explanatory videos. Developed through a university-business collaboration between the University of Essex and Hub, with support from Innovate UK’s Knowledge Transfer Partnership (grant agreement No. 11071), This is  a collection of commercial advertisements and corresponding ground-truth files based on the measurement of long-term memory performance when recognizing them from 24 to 72 hours after having viewed them.  Each video is accompanied with metadata such as titles, descriptions, number of views, and duration and audio and video features extracted from the commercial advertisements. The dataset consists of 424 commercial videos sampled from a larger collection of 4791 videos published on YouTube between June 2018 and June 2021. Video lengths range from 7 seconds to 94 minutes. For longer videos, users are allowed to watch only 1 minute. 
 
 #### Evaluation methodology