2016-12-14: Links to the workshop papers and slides are available. Check the Program tab
The 1st Workshop on Large Scale Computer Vision Systems
December 10, 2016, Barcelona, SPAIN

Computer Vision is a mature field with a long history of academic research. Recent advances in deep learning have provided machine learning models with new capabilities to understand visual content. There have been tremendous improvements on a variety of problems, such as classification, detection and segmentation, which are basic proxies for the ability of a model to understand the visual content. These advances have been accompanied by a steep rise of Computer Vision adoption in industry at scale, and by the introduction of more complex tasks such as Image Captioning and Visual Q&A. As industrial applications mature, the challenges slowly shift towards challenges in data, in scale, and in moving from purely visual data to multi-modal data. 

An especially relevant emerging topic in Computer Vision is video understanding, which aims at developing computer methods that can interpret videos at different semantic levels. Applications include video categorization, event detection, semantic segmentation, description, summarization, tagging, content-­based retrieval, surveillance, and many more. Although in the last two decades the field of video analytics has witnessed significant progress, most problems in this area still remain largely unsolved. In recent years video understanding has become an even more critical and timely problem to address because of the tremendous growth of videos on the Internet, most of which do not contain tags or descriptions and thus necessitate automatic analysis to become searchable or browsable. At the same time the rise of online video repositories represents an opportunity for the creation of new pivotal large­-scale datasets for research in this area. Given the recent breakthroughs achieved by deep learning in other big data domains, we believe that video understanding may very well be on the verge of a technical revolution that will spur significant advances in this area.

The Large Scale Computer Vision systems brings together researchers and practitioners who are interested to address this new set of challenges and provide a venue to share how industry and academia approach these problems. 

Invited Speakers
  • Cordelia Schmid, INRIA
  • Abhinav Gupta, CMU
  • Raquel Urtasun, University of Toronto
  • Zehan Wang, Twitter
  • Antonio Torralba, MIT

  • Lorenzo Torresani, Dartmouth
  • Gal Chechik, Google
  • Du Tran, Facebook
  • Manohar Paluri, Facebook
  • Dario Garcia, Facebook