Call for Papers

Papers are sought in the following areas:

  • Use of multi-modal information in image tasks (e.g., text, GPS tags, timestamps, social network, implicit feedback, audio, user preferences)
  • Image tasks beyond object classification -- that is, novel applications (comprehensive scene understanding, object discovery, attribute learning, aesthetic analysis, modeling of the collective structure of large-scale image datasets, etc.)
  • Novel learning techniques and features especially suited for the above applications
  • Papers that emphasize on integrated learning approaches, in contrast to solving any issues purely via complex software engineering (i.e., by chaining standard methods).
  • Methods that are truly scalable to millions of images and/or to large video repositories, which now dominate many vision tasks.
  • Algorithms that really push the boundaries of Machine Learning for Computer Vision tasks, or applications which really push the boundaries of both disciplines are particularly sought.

The program committee will review papers and provide suggestions for either a  poster or oral presentation. Note that scientific contribution is a must;  however, we encourage preliminary approaches that partially solve a challenging issue, or  solutions that target a problem of interest but are not necessarily state-of-the-art in terms of performance (e.g., a method that scales to 1 trillion images on a mobile  phone, but is 2% behind the winner on the latest vision challenge so would not necessarily be considered 'state of the art'). The aim of the workshop is to look to the future, as much as it is to demonstrate successes of the (recent) past.

Call for Demos/Projects

We especially solicit posters and/or demos from projects (e.g. internal, NSF funded, EU projects). This can be from projects near completion -- an opportunity to show the community what challenges were addressed and demonstrate and software / datasets / systems that were produced. Alternatively, these can form outlines, ideas, open problems. The idea is to raise awareness of all activities in the joint area of machine learning / computer vision among as many researchers as possible. We will aim to accommodate as many relevant demos/project posters as possible.

Our overall aim: is to promote fruitful discussion among researchers from both communities, to raise awareness of work / challenges / projects / datasets, and to provide a relaxed environment in which to discuss these aspects. We are not aiming at a processional mini-conference, the outcome of the workshop should be more than a list of papers to go and read: hopefully you will have new contacts and new research ideas to get very excited about.

Submission Instructions

  • Regular papers should fit on 8 pages maximum formatted according to the standard NIPS template, and do not include author  information.
  • For Demos/Projects please submit a maximum 4 page description of the project in a NIPS paper style format. Give a brief overview of the project, the particular topic of a poster/demo, and describe why you feel it is relevant. 
  • The NIPS style files can be found here.
  • Papers should be submitted through the CMT system.


Important Dates

  • Paper submission: 23:59, GMT, 25th October, 2010
  • Author notification: 4th November 2010, reviews and decisions available in CMT