Connectionists: CFP NIPS*05 Interclass Transfer Workshop: why learning to recognize many objects might be easier than learning to recognize just one?

[Michael Fink]

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Call for Papers

NIPS*05 Workshop on interclass transfer: 
Why learning to recognize many object classes might
be easier than learning to recognize just one

www.cs.huji.ac.il/~fink/nips2005/<http://www.cs.huji.ac.il/%7Efink/nips2005/>

NIPS 2005

Submission deadline: 21 October
Accept/Reject notification: 05 November

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Organizers:
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Andras Ferencz, University of California at Berkeley
Michael Fink, The Hebrew University of Jerusalem
Shimon Ullman, Weizmann Institute of Science


Workshop Description
====================

The human perceptual system has the remarkable capacity to recognize 
numerous
object classes, often learning to reliably classify a novel category from 
just
a short exposure to a single example. These skills are beyond the reach of
current multi-class recognition systems. The workshop will focus on the
proposal that a key factor for achieving such capabilities is the use of
interclass transfer during learning. According to this view, a recognition
system may benefit from interclass transfer if the multiple target
classification tasks share common underlying structures that can be utilized 
to
facilitate training or detection. Several challenges follow from this
observation. First, can a theoretical foundation of interclass transfer be
formulated? Second, what are promising algorithmic approaches for utilizing
interclass transfer. Finally, can the computational approaches for multiple
object recognition contribute insights to the research of human recognition
processes?

In the coming workshop we propose to address the following topics:

* Explore the human capabilities for multi-class object recognition and 
examine
how these capacities motivate our algorithmic approaches.

* Attempt to formalize the interclass transfer framework and define what can 
be
generalized between classes (for example, learning by analogy from the
"closest" known category vs. finding useful subspaces from all categories).

* Analyze state-of-the-art solutions aimed at recognizing many objects or at
learning to recognize novel objects form very few examples (e.g. contrasting
parametric vs. non-parametric approaches).

* Characterize the problems in which we expect to observe high transfer 
between
classes.

* Delineate future challenges and suggest benchmarks for assessing progress

The workshop is aimed at bringing together experimental and theoretical
researchers interested in multi-class object recognition in humans and
machines. 


Confirmed participants:
=======================
William T. Freeman
Fei Fei Li
Erik Learned Miller
Kevin Murphy
Jitendra Malik
Antonio Torrallba
Daphna Weinshall