- The workshop program is now available.
- October 10: full paper notifications have been sent.

Important Dates
Workshop: December 18, 2015
Submission (extended abstracts of published works, 2 pages): September 30, 2015  October 15, 2015
Submission (full papers, 6-8 pages): September 15, 2015  September 25, 2015
Decisions to Authors: October 9, 2015
Camera-ready Deadline: October 14, 2015

Call for submission
The Web has become a large ecosystem that reaches billions of users through information processing and sharing, and most of this information resides in pixels. Web-based services like YouTube and Flickr, and social networks such as Facebook have become increasingly popular, enabling users to easily upload, share and annotate massive numbers of images and videos.
Although this so-called Web 2.0 contains a wealth of visual content, most online social platforms still rely primarily on user tags and text-based search to organize the information.  There is a critical need for novel, scalable methods that can understand this visual data and exploit (noisy) user annotations in order to enable users to better navigate the available multimedia content.
Thus, the combination of vision and social media has become a very active interdisciplinary research area, involving computer vision, multimedia, machine-learning, information retrieval, and data mining.
The VSM workshop aims to bring together leading researchers in these related fields to advocate and promote new research directions for problems involving web vision and social media, such as large-scale visual content analysis, search and mining. VSM will provide an interactive platform for academic and industry researchers to disseminate their most recent results, discuss potential new directions in vision and social media, and promote new interdisciplinary collaborations. The program will consist of invited talks, panels, discussions, and reviewed paper submissions.

Topics of interest include (but are not limited to):
  • Content analysis for vision and social media
  • Efficient learning and mining algorithms for large-scale vision and social media analysis
  • Understanding social media content and dynamics
  • Contextual models for computer vision and social media
  • Machine learning and data mining for social media
  • Indexing and retrieval for large-scale social media information
  • Tagging, semantic annotation, and object recognition on massive multimedia collections 
  • Scalable and distributed machine learning and data mining methods for vision
  • Interfaces for exploring, browsing and visualizing large visual collections
  • Construction and evaluation of large‐scale visual collections
  • Crowdsourcing for vision problems
  • Scene reconstruction and matching using large scale web images