Joint analysis of shapes and images via deep domain adaptation.

3D shapes and 2D images usually contain complementary information for each other, and thus joint analysis of both of them will benefit some problems existing in different domains. Leveraging the connection between 2D images and 3D shapes, it's potential to mine lacking information of one modal from the other. Stemming from this insight, we design and implement a CNN architecture to jointly analyze shapes and images even with few training data guidance. The core of our architecture is a domain adaptation algorithm, which builds up the connection between underlying feature spaces of images and shapes, then aligns and correlates the intrinsic structures therein. The proposed method facilitates the recognition and retrieval tasks. Experiments on the shape recognition tasks show that our approach has superior performance under the difficult setting: zero-shot learning and few-shot learning. We also evaluate our method on the retrieval tasks, and demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]