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From:
Demarty Claire-Helene <[log in to unmask]>
Reply To:
Demarty Claire-Helene <[log in to unmask]>
Date:
Sun, 14 Jan 2018 16:55:14 +0000
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[Apologies for cross-posting]



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First Call for Papers

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Special session "Predicting User Perceptions of Multimedia Content at ACM International Conference on Multimedia Retrieval (ICMR2018)

11-14 June 2018

Yokohama, Japan

http://research.cs.aalto.fi/cbir/icmr2018-specialsession/

http://www.icmr2018.org/



# Call for Papers #

Understanding of multimedia content by machines has reached an important milestone in current research with the latest achievements in machine learning. However, although such technologies may enable a high level understanding of multimedia scenes, their output still remains a factual and plain description of it. What is related to human perceptions of media content is still most of the time eluded, although there have been important breakthroughs in analyzing subjective properties, such as aesthetics, memorability, or induced emotions.


Predicting user perceptions of multimedia content aims to go one step forward in putting human subjectivity and interpretation in the centre of the scene understanding, by rending the systems able to automatically predict human-like concepts such as interestingness, affective values and emotions, aesthetic values, memorability, novelty, complexity, visual composition and stylistic attributes, creativity, etc. These are specifically interesting for a very broad range of current applications, e.g., content retrieval and search, storytelling, targeted advertising, education and learning, and content filtering.


Work already exists in the literature that studies the psychological aspects of these notions or investigates potential correlations between two or more of these human concepts. Attempts at building computational models capable of predicting such notions can also be found, which use state-of-the-art machine learning techniques. Nevertheless their performance proves that there is still room for improvement, as the task is by its nature highly challenging and multifaceted because it has to address on one hand, the psychological implications of the human concepts, and on the other hand their translation to machines, meaning the availability of appropriate labeled data and specially tuned algorithms.


In a special session at ICMR18, we invite contributions that focus on this specific topic with a special emphasis on the prediction of interestingness and affect of media content, as well as their covariates. More specifically, the topics of the special session include (but are not limited to):


  *   Data collection, annotation and evaluation methods
  *   Computational models for predicting user perceptions of multimedia content
  *   Retrieval and search models that incorporate prediction of user perceptions
  *   Content filtering and recommendation based on user perceptions of content
  *   Video segmentation and summarization based on user perceptions of content
  *   Deep learning models for predicting user perceptions of multimedia content
  *   Prediction and modeling of content interestingness and covariates
  *   Content-driven and social-driven interestingness prediction
  *   Influence of context on interestingness prediction
  *   Temporal computational models for capturing emotional changes along videos
  *   Applications of content understanding based on user perceptions


# Important Dates #

- Paper Submission: February 17, 2018

- Notification of Acceptance: March 30, 2018

- Camera-Ready Papers Due: April 27, 2018


# Submission Instructions #

- Single-Blind Review

- Maximum Length of a Paper: Each full paper should not be longer than 8 pages (ACM proceedings style).

- Submission link:  https://easychair.org/conferences/?conf=icmr18


# Special Session Organisers #

- Claire-Helene Demarty (main contact) and Ngoc Q. K. Duong, Technicolor, France

- Emmanuel Dellandrea, Ecole Centrale Lyon, France

- Mats Sjberg, Aalto University, Finland

- Bogdan Ionescu, University Politehnica of Bucharest, Romania

- Thanh-Toan Do, University of Adelaide, Australia

- Yoann Baveye, Capacit駸, France




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