ImageCLEF 2021 Aware

Note: Do not forget to read the Rules section on this page. Pressing the red Participate button leads you to a page where you have to agree with those rules. You will not be able to submit any results before agreeing with the rules.

Note: Before trying to submit results, read the Submission instructions section on this page.

Challenge description

Images constitute a large part of the content shared on social networks. Their disclosure is often related to a particular context and users are often unaware of the fact that, depending on their privacy status, images can be accessible to third parties and be used for purposes which were initially unforeseen. For instance, it is common practice for employers to search information about their future employees online. Another example of usage is that of automatic credit scoring based on online data. Most existing approaches which propose feedback about shared data focus on inferring user characteristics and their practical utility is rather limited.

We hypothesize that user feedback would be more efficient if conveyed through the real-life effects of data sharing. The objective of the task is to automatically score user photographic profiles in a series of situations with strong impact on her/his life. Four such situations were modeled this year and refer to searching for: (1) a bank loan, (2) an accommodation, (3) a job as waitress/waiter and (4) a job in IT. The inclusion of several situations is interesting in order to make it clear to the end users of the system that the same image will be interpreted differently depending on the context.

Given the training dataset described below, participants will propose machine learning techniques which provide a ranking of test user profiles in each situation which is as close as possible to a human ranking of the test profiles.

Data

This is the first edition of the task. A data set of 500 user profiles with 100 photos per profile was created and annotated with an "appeal" score for a series of real-life situations via crowdsourcing.
Participants to the experiment were asked to provide a global rating of each profile in each situation modeled using a 7-points Likert scale ranging from "strongly unappealing" to "strongly appealing".
The averaged "appeal" score will be used to create a ground truth composed of ranked users in each modeled situation.
User profiles are created by repurposing a subset of the YFCC100M dataset.

In accordance with GDPR, data minimization is applied and participants receive only the information necessary to carry out the task in an anonymized form.
Resources include (i) anonymized visual concept ratings for each situation modeled; (ii) automatically extracted predictions for the images that compose the profiles.
The final objective of the task is to integrate the most promising of the developed algorithms into YDSYO (https://ydsyo.app), a mobile app that provides situation-related feedback to users.

As soon as the data is released it will be available under the "Resources" tab.

Submission instructions

As soon as the submission is open, you will find a “Create Submission” button on this page (next to the tabs).

Before being allowed to submit your results, you have to first press the red participate button, which leads you to a page where you have to accept the challenge's rules.

Participants to the task will provide an automatically ranking of user ratings for each situation which will be compared to a ground truth rating obtained by crowdsourcing (see "Data" section below).
The correlation between the two ranked list will be measured using Pearson's correlation coefficient.
The final score of each participating team will be obtained by averaging correlations obtained for individual situations.

Participants will be permitted to submit up to 10 runs. External training data is not allowed.

More information will be added soon!

Rules

Note: In order to participate in this challenge you have to sign an End User Agreement (EUA). You will find more information on the 'Resources' tab.

ImageCLEF lab is part of the Conference and Labs of the Evaluation Forum: CLEF 2021. CLEF 2021 consists of independent peer-reviewed workshops on a broad range of challenges in the fields of multilingual and multimodal information access evaluation, and a set of benchmarking activities carried in various labs designed to test different aspects of mono and cross-language Information retrieval systems. More details about the conference can be found here.

Submitting a working note with the full description of the methods used in each run is mandatory. Any run that could not be reproduced thanks to its description in the working notes might be removed from the official publication of the results. Working notes are published within CEUR-WS proceedings, resulting in an assignment of an individual DOI (URN) and an indexing by many bibliography systems including DBLP. According to the CEUR-WS policies, a light review of the working notes will be conducted by ImageCLEF organizing committee to ensure quality. As an illustration, ImageCLEF 2020 working notes (task overviews and participant working notes) can be found within CLEF 2020 CEUR-WS proceedings.

Important

Participants of this challenge will automatically be registered at CLEF 2021. In order to be compliant with the CLEF registration requirements, please edit your profile by providing the following additional information:

• First name

• Last name

• Affiliation

• Address

• City

• Country

• Regarding the username, please choose a name that represents your team.

This information will not be publicly visible and will be exclusively used to contact you and to send the registration data to CLEF, which is the main organizer of all CLEF labs

Participating as an individual (non affiliated) researcher

We welcome individual researchers, i.e. not affiliated to any institution, to participate. We kindly ask you to provide us with a motivation letter containing the following information:

• the presentation of your most relevant research activities related to the task/tasks

• your motivation for participating in the task/tasks and how you want to exploit the results

• a list of the most relevant 5 publications (if applicable)

• the link to your personal webpage

The motivation letter should be directly concatenated to the End User Agreement document or sent as a PDF file to bionescu at imag dot pub dot ro. The request will be analyzed by the ImageCLEF organizing committee. We reserve the right to refuse any applicants whose experience in the field is too narrow, and would therefore most likely prevent them from being able to finish the task/tasks.

Citations

Information will be posted after the challenge ends.

Prizes

Publication

ImageCLEF 2021 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.

Resources

Contact us

Discussion Forum

• You can ask questions related to this challenge on the Discussion Forum. Before asking a new question please make sure that question has not been asked before.
• Click on Discussion tab above or direct link: https://www.aicrowd.com/challenges/imageclef-2021-aware/discussion

Alternative channels

We strongly encourage you to use the public channels mentioned above for communications between the participants and the organizers. In extreme cases, if there are any queries or comments that you would like to make using a private communication channel, then you can send us an email at :

• adrian[dot]popescu[at]cea[dot]fr

More information

You can find additional information on the challenge here: https://www.imageclef.org/2021/aware

This task is supported under project AI4Media, A European Excellence Centre for Media, Society and Democracy, H2020 ICT-48-2020, grant #951911.