Program Chair

JIa Li


The engagement of multimedia with society as whole requires research that addresses how multimedia can be used to connect people with multimedia artifacts that meet their needs in a variety of contexts.


Multimedia Art, Entertainment and Culture
Multimedia Search and Recommendation

Big Data

Digital Society

Multimedia Technology for Autonomous Vehicles

publicity chair

Juan Carlos Niebles
Stanford University

Plenary Talk

Fei-Fei Li
Stanford University

TPC Members


Area: Multimedia Art, Entertainment and Culture

Area Chairs

Rossano Schifanella
University of Turin
James Wang
The Pennsylvania State University

Multimedia plays a significant role in engaging the public with art and other forms of cultural expression, as well as a tool that provides rich entertainment experiences to users. A key challenge is therefore to develop techniques that enable effective engagement within these applications. The focus of this area is on the innovative use of digital multimedia technology in arts, entertainment and culture, to support the creation of multimedia content, artistic interactive and multimodal installations, the analysis of media consumption and user experience, or cultural preservation.

We seek full and short papers in a broad range of integrated artistic and scientific statements describing digital systems for arts, entertainment, and culture. Successful papers should achieve a balance between sophisticated technical content and artistic or cultural purpose.

Topics of interest include, but are not limited to:

  • Models of interactivity specifically addressing arts and entertainment
  • Active experience of multimedia artistic content by means of socio-mobile multimodal systems
  • Analysis of spectator experience in interactive systems or digitally-enhanced performances
  • Virtual and augmented reality artworks, including hybrid physical/digital installations
  • Dynamic, generative and interactive multimedia artworks
  • Creativity support tools
  • Computational aesthetics in multimedia and multimodal systems
  • Tools for or case studies on cultural preservation or curation

Area: Multimedia Search and Recommendation

Area Chairs

Liangliang Cao
Michele Merler
IBM Research
Yannis Avrithis
Inria Rennes
Herve Jegou

Navigating, indexing, searching and discovering content in large collections of multimedia is a key concern for users and service providers. In spite of great progress in this area, several technical challenges still persist, in particular with respect to performing semantically aware search and recommendations. In the past decade, there has been an explosive growth of multimedia contents on the Web, desktops, and mobile devices. The deluge of multimedia leads to “information overload” and poses new challenges and requirements for effective and efficient access to multimedia content. Multimedia search and recommendation techniques are essential in order to provide information relevant to users’ information needs.

This area calls for contributions on reporting novel problems, solutions, models, and/or theories that tackle the key issues in searching, recommending, and discovering multimedia content, as well as a variety of multimedia applications based on search and recommendation technologies.

Topics of interest include, but are not restricted to:

  • Large-scale multimedia indexing, ranking, and re-ranking
  • Novel representation and scalable quantization for efficient multimedia retrieval
  • Interactive and collaborative multimedia search
  • Search, ranking and recommendation for social media data
  • User intent modeling, query suggestion and feedback mechanisms
  • Multimedia search in specific domains (e.g., scientific, enterprise, social, fashion)
  • Summarization and organization of multimedia collections
  • Knowledge discovery from massive multimedia data
  • Data representations for recommendation tasks
  • Multimedia-focused recommendation models
  • Cross-modal recommendations
  • Personalization in recommendation
  • New interaction strategies for recommendation
  • Diversity in search and recommendation
  • Generalization of recommendations to new users and/or new content (cold start)

Area: Big Data

Area Chairs

Jiebo Luo
University of Rochester

As multimedia collections and services grow, the amount of multimedia data approaches a large scale regime. With big data, new challenges and opportunities emerge. For example, rare patterns in data can become more evident, but more efficient processing is required. Over the past few years, tremendous amount of multimedia data is generated everyday with the advancement of digital devices. Meanwhile, machine learning on large scale multimedia data has become a thriving field with a plethora of theories and tools developed.

The goal is to encourage large scale multimedia data to be shared in the community, to engage researchers to work on large scale multimedia problems, to inform researchers about new developments on large scale multimedia problems, and to identify unique challenges and opportunities.

Topics of interest include, but are not restricted to:

  • Novel big multimedia data in various fields (e.g., finance, content & entertainment, search, healthcare, life sciences, manufacturing, IoT, transportation, and retail)
  • Novel applications to big multimedia data in various fields (e.g., finance, content & entertainment, search, healthcare, life sciences, manufacturing, IoT, transportation, and retail)
  • Large Scale perception problems
  • High efficiency compression, coding and transmission of multimedia big data
  • Large scale cloud computing
  • Green computing for multimedia big data
  • Interaction, access, visualization of large scale multimedia data

Area: Digital Society

Area Chairs

Tao Mei
Lamberto Ballan
Stanford University
Peng Cui
Tsinghua University

This area seeks novel contributions investigating online social interactions around multimedia systems, streams, and collections. Social media (such as Facebook, Twitter, Flickr, YouTube etc.) has substantially and pervasively changed the communication among organizations, communities, and individuals. Sharing of multimedia objects, such as images, videos, music, associated text messages, and recently even digital activity traces such as fitness tracking measurements, constitutes a prime aspect of many online social systems nowadays. This gives us valuable opportunities to understand user-multimedia interaction mechanisms, to predict user behavior, to model the evolution of multimedia content and social graphs, or to design human-centric multimedia applications and services informed by social media, like analysing and predicting related real-world phenomena.

The submissions in this area should look specifically at methods and systems wherein social factors, such as user profiles, user behaviors and activities, and social relations are organically integrated with online multimedia data to understand media content, media use in an online social environment. Or they should leverage the socially created data to solve challenging problems in traditional multimedia computing, enable applications addressing real-world problems (e.g. sales prediction, brand and environmental monitoring) or address new research problems emergent in the online social media scenario.

The proposed contributions are expected to scale up to serve large online user communities. They should exploit massive online collective behavior by looking at e.g., large-group online interactions and group sentiments aggregated across many users in an online community. They should also be able to handle large, heterogeneous and noisy multimedia collections typical for social media environments. Special emphasis is put on multimodal approaches leveraging multiple information sources and modalities to be found in the social media context.

Topics of interest include, but are not restricted to:

  • Social media data collection, filtering, and indexing
  • Social media data representation and understanding
  • User profiling from social media
  • Personal information disclosure and privacy aspects of social media
  • Modeling collective behavior in social media
  • Multimedia propagation in online social environments
  • Spatial-temporal context analysis in social media
  • Monitoring, sensing, prediction and forecasting applications with social media
  • Multimedia-enabled social sharing of information
  • Detection and analysis of emergent events in social media collections
  • Verification of social media content
  • Evaluation of user engagement around shared media
  • Convergence between Internet of Things, wearables and social media
  • Systems and analysis of location-based social media
  • Network theory and algorithms in social multimedia systems
  • Models for the spatio-temporal characteristics of social media
  • Models and systems for analyzing large-scale online sentiments

Area: Multimedia Technology for Autonomous Vehicles

Area Chairs

As smart vehicles, robots and smart environments evolve, there are new opportunities for providing rich entertainment and engagement to users. These new platforms will probably require different approaches in comparison to traditional devices. Technology enabling multimedia systems and tackling multimedia problems are essential to autonomous vehicles.

This area calls for contributions on reporting novel problems, solutions, models, and/or theories that tackle the key issues in autonomous vehicles area, as well as a variety of multimedia and machine learning applications powering autonomous vehicles technologies.

Topics of interest include, but are not restricted to:

  • Multimedia based interaction between human and autonomous vehicles
  • Autonomous navigation and exploration
  • Sensor-based advanced driver assistance systems
  • Real-time perception and prediction of traffic scenes
  • Efficient pedestrian and object detection
  • Scene classification and environment understanding
  • Sensor-based underwater and unmanned aerial vehicles
  • Visual driver monitoring and driver-vehicle interfaces
  • Performance evaluation of vehicular applications
  • Deep learning and other machine learning techniques for vehicle technology
  • Sensor based geo-localization