Domain Adaptation for Structure Recognition in Different Building Styles

Many learning-based computer vision algorithms perform poorly when faced with examples that are dissimilar to those on which they were trained. Domain adaptation methods attempt to address this problem, but usually assume that the source domain is specified a priori. We propose a two-step approach for situations where more than one source domain is available. The first step uses a small number of labeled examples to choose the source domain most similar to the target domain, while the second step uses traditional domain adaptation methods to further adapt the chosen source domain to the target data. We demonstrate this two-step domain adaptation algorithm in the context of style-independent building component recognition, which suffers from the problem of inter-domain performance degradation. In this case, different building styles represent the domains, and the task is to reverse engineer a new building of unknown style. We evaluate several variants of the two-step method, and experiments show that the proposed approach outperforms existing single-step methods on a dataset of nine building styles. We demonstrate the generality of the approach on a large, multi-domain dataset with 22 product review categories (i.e., Styles) from the natural language processing field.