Model Recommendation for Action Recognition
Venue
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR'12) (2012)
Publication Year
2012
Authors
Pyry Matikainen, Rahul Sukthankar, Martial Hebert
BibTeX
Abstract
Simply choosing one model out of a large set of possibilities for a given vision
task is a surprisingly difficult problem, especially if there is limited evaluation
data with which to distinguish among models, such as when choosing the best
``walk'' action classifier from a large pool of classifiers tuned for different
viewing angles, lighting conditions, and background clutter. In this paper we
suggest that this problem of selecting a good model can be recast as a
recommendation problem, where the goal is to recommend a good model for a
particular task based on how well a limited probe set of models appears to perform.
Through this conceptual remapping, we can bring to bear all the collaborative
filtering techniques developed for consumer recommender systems (e.g., Netflix,
Amazon.com). We test this hypothesis on action recognition, and find that even when
every model has been directly rated on a training set, recommendation finds better
selections for the corresponding test set than the best performers on the training
set.
