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ACCV
2010
Springer

Unsupervised Selective Transfer Learning for Object Recognition

9 years 11 months ago
Unsupervised Selective Transfer Learning for Object Recognition
Abstract. We propose a novel unsupervised transfer learning framework that utilises unlabelled auxiliary data to quantify and select the most relevant transferrable knowledge for recognising a target object class from the background given very limited training target samples. Unlike existing transfer learning techniques, our method does not assume that auxiliary data are labelled, nor the relationships between target and auxiliary classes are known a priori. Our unsupervised transfer learning is formulated by a novel kernel adaptation transfer (KAT) learning framework, which aims to (a) extract general knowledge about how more structured objects are visually distinctive from cluttered background regardless object class, and (b) more importantly, perform selective transfer of knowledge extracted from the auxiliary data to minimise negative knowledge transfer suffered by existing methods. The effectiveness and efficiency of the proposed approach is demonstrated by performing one-class ob...
Wei-Shi Zheng, Shaogang Gong, Tao Xiang
Added 12 May 2011
Updated 12 May 2011
Type Journal
Year 2010
Where ACCV
Authors Wei-Shi Zheng, Shaogang Gong, Tao Xiang
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