Most metric learning methods utilize all training data to construct a single metric, and it is usually over-fitting on the "salient" feature. To overcome this issue, we propose a deep meta metric learning method based on collaborative representation. We construct multiple episodes from the original training data to train a general metric, where each episode consists of a query set and a support set. Then, we introduce a collaborative representation method, which fits the query sample with the support samples per class. We predict the query sample's label via the optimal fitness among the query sample and the support samples in each specific class. Besides, we adopt a hard mining strategy to learn a more discriminative metric according to increasing the training tasks' difficulty. Experiments verify that our method achieves state-of-the-art results on three re-ID benchmark datasets.

Domain adaptation has received lots of attention for its high efficiency in dealing with cross-domain learning tasks. Most existing domain adaptation methods adopt the strategies relying on large amounts of source label information, which limits their applications in the real world where only a few label samples are available. We exploit the local geometric connections to tackle this problem and propose a Local Structure Alignment (LSA) guided domain adaptation method in this paper. LSA leverages the Nyström method to describe the distribution difference from the geometric perspective and then perform the distribution alignment between domains. Specifically, LSA constructs a domain-invariant Hessian matrix to locally connect the data of the two domains through minimizing the Nyström approximation error. And then it integrates the domain-invariant Hessian matrix with the semi-supervised learning and finally builds an adaptive semi-supervised model. Extensive experimental results validate that the proposed LSA outperforms the traditional domain adaptation methods especially when only sparse source label information is available.