Despite the increasing interest towards domain adaptation and transfer learning techniques to generalize over image collections and overcome their biases, the visual community misses a large scale testbed for cross-dataset analysis. In this paper we discuss the challenges faced when aligning twelve existing image databases in a unique corpus, and we propose two cross-dataset setups that introduce new interesting research questions. Moreover, we report on a first set of experimental domain adaptation tests showing the effectiveness of iterative self-labeling for large scale problems. © Springer International Publishing Switzerland 2015.
A Testbed for Cross-Dataset Analysis / Tommasi, Tatiana; Tuytelaars, Tinne. - 8927:(2015), pp. 18-31. (Intervento presentato al convegno 13th European Conference on Computer Vision, ECCV 2014 tenutosi a Zurich; Switzerland) [10.1007/978-3-319-16199-0_2].
A Testbed for Cross-Dataset Analysis
Tommasi, Tatiana
;
2015
Abstract
Despite the increasing interest towards domain adaptation and transfer learning techniques to generalize over image collections and overcome their biases, the visual community misses a large scale testbed for cross-dataset analysis. In this paper we discuss the challenges faced when aligning twelve existing image databases in a unique corpus, and we propose two cross-dataset setups that introduce new interesting research questions. Moreover, we report on a first set of experimental domain adaptation tests showing the effectiveness of iterative self-labeling for large scale problems. © Springer International Publishing Switzerland 2015.| File | Dimensione | Formato | |
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