IEEE Workshop on Models versus Exemplars in Computer Vision to be held in conjunction with CVPR 2001 in Kauai, Hawaii (http://www.cs.berkeley.edu/~leungt/cvpr01_workshop.html) CALL FOR PAPERS The variety of models of shape and intensity/color distributions used in computer vision could be regarded as lying along a spectrum. At one extreme, the model space is continuous, consisting of objects with explicit, continuous parameterizations. Examples include: spline-based models of shape and eigenspace models of shading. At the other extreme are discrete sets of "exemplars." Examples include: modeling texture as random samples from a collection of exemplar patches and modelling a walking person as a set of exemplar outlines. As an illustration of the two paradigms, consider how one might represent a dancer: a model-based approach might construct an articulated 3D model with texture-mapped limbs and hand-specified degrees of freedom; an exemplar-based representation, on the other hand, might consist of images of the dancer in M different poses from N vantage points. Each kind of representation has its advantages and disadvantages. Parameterized models have the advantage of sparse representation and clear correspondence with semantics and underlying physical phenomena; on the other hand, they need to be laboriously constructed by hand or built from pre-annotated training data, and search over parameters suffers combinatorial growing pains with increasing model dimensionality. Exemplar-based representations, in contrast, are easy to create, and they are capable of representing nonlinear manifolds efficiently. Disadvantages include a lack of continuous structure to represent physical variables and inefficiency in space-complexity. The goal of this workshop is to examine the inherent strengths and weaknesses of these two kinds of representation. We invite papers that address issues related to the following (with preference for domains other than object recognition, where the "object-based versus view-based" discussion has covered similar ground): * Automated construction of models from unannotated or partially annotated training data. * Algorithms for embedding exemplars in structured spaces. * Algorithms which alleviate the problems of one or the other approach. * Comparisons of model-based and exemplar-based techniques applied to the same task. * Analysis of algorithm complexity, for models or exemplars, with increased dimensionality. * Hybrid techniques for combining model- and exemplar-based methods. In order to incite debate and discussion, we will allocate time for multiple panel discussions during the workshop, with paper presenters and invited speakers as panelists. IMPORTANT DATES (subject to minor changes): August 17, 2001 Electronic paper submission [PDF] (one week after CVPR 2001 notifications) Send submissions to mailto:exemplar@microsoft.com 8 pages maximum. September 14, 2001 Notification of acceptance/rejection October 12, 2001 Final electronic submission due [PDF] December 14, 2001 Workshop (following CVPR 2001, Kauai, Hawaii) ORGANIZING COMMITTEE: Andrew Blake Microsoft Research Cambridge ablake@microsoft.com Thomas Leung Compaq Cambridge Research Lab tleung@crl.dec.com James Rehg Compaq Cambridge Research Lab rehg@crl.dec.com Kentaro Toyama Microsoft Research Redmond kentoy@microsoft.com PROGRAM COMMITTEE (for affiliations, please see web page): George Bebis Larry Davis Jitendra Malik Matt Brand Brendan Frey Jianbo Shi Tim Cootes Dariu Gavrila Thomas Vetter Trevor Darrell David Kriegman Song-Chun Zhu