VSM 2015 - 3rd Workshop on Web-scale Vision and Social Media (VSM)

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