Submission deadline: 21 October
Acceptance notification: 05 November
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 NIPS05 interclass transfer 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 with 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. A workshop aimed at providing a machine learning perspective on Inductive Transfer will be held on Sat. Dec 9.