Transfer discriminant-analysis of canonical correlations for view-transfer action recognition
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Beyond dataset bias: multi-task unaligned shared knowledge transfer
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Cross-Database transfer learning via learnable and discriminant error-correcting output codes
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Domain transfer for person re-identification
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
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The vast majority of transfer learning methods proposed in the visual recognition domain over the last years addresses the problem of object category detection, assuming a strong control over the priors from which transfer is done. This is a strict condition, as it concretely limits the use of this type of approach in several settings: for instance, it does not allow in general to use off-the-shelf models as priors. Moreover, the lack of a multiclass formulation for most of the existing transfer learning algorithms prevents using them for object categorization problems, where their use might be beneficial, especially when the number of categories grows and it becomes harder to get enough annotated data for training standard learning methods. This paper presents a multiclass transfer learning algorithm that allows to take advantage of priors built over different features and with different learning methods than the one used for learning the new task. We use the priors as experts, and transfer their outputs to the new incoming samples as additional information. We cast the learning problem within the Multi Kernel Learning framework. The resulting formulation solves efficiently a joint optimization problem that determines from where and how much to transfer, with a principled multiclass formulation. Extensive experiments illustrate the value of this approach.