Visual-Depth Matching Network: Deep RGB-D Domain Adaptation With Unequal Categories.

Existing domain adaptation (DA) methods generally assume that different domains have identical label space, and the training data are only sampled from a single domain. This unrealistic assumption is quite restricted for real-world applications, since it neglects the more practical scenario, where the source domain can contain the categories that are not shared by the target domain, and the training data can be collected from multiple modalities. In this article, we address a more difficult but practical problem, which recognizes RGB images through training on RGB-D data under the label space inequality scenario.

Semi-Supervised Multi-View Deep Discriminant Representation Learning.

Learning an expressive representation from multi-view data is a key step in various real-world applications. In this paper, we propose a semi-supervised multi-view deep discriminant representation learning (SMDDRL) approach. Unlike existing joint or alignment multi-view representation learning methods that cannot simultaneously utilize the consensus and complementary properties of multi-view data to learn inter-view shared and intra-view specific representations, SMDDRL comprehensively exploits the consensus and complementary properties as well as learns both shared and specific representations by employing the shared and specific representation learning network.

Adaptive RGB Image Recognition by Visual-Depth Embedding.

Recognizing RGB images from RGB-D data is a promising application, which significantly reduces the cost while can still retain high recognition rates. However, existing methods still suffer from the domain shifting problem due to conventional surveillance cameras and depth sensors are using different mechanisms. In this paper, we aim to simultaneously solve the above two challenges: 1) how to take advantage of the additional depth information in the source domain? 2) how to reduce the data distribution mismatch between the source and target domains? We propose a novel method called adaptive Visual- Depth Embedding (aVDE) which learns the compact shared latent space between two representations of labeled RGB and depth modalities in the source domain first.