Many object re-identification (Re-ID) methods that depend on large-scale training datasets have been proposed in recent years. However, the performance of these methods degrades dramatically when insufficient training data are available. To address this challenging problem, we propose a few-shot object re-identification (FSOR) method that enhances the generalization and discrimination abilities of object Re-ID models trained on small datasets. This method applies two novel techniques: Reparameterization for feature vectors and dual-distance metric learning. The reparameterization mechanism transforms the primary feature vector of each input image into a Gaussian distribution to enhance the robustness of the FSOR method when performing object Re-ID tasks. The dual-distance metric learning technique, called HC learning, considers both the hard mining distance and the center-point distance between each query sample and each support set of different object identities. HC learning extracts the characteristics of the entire training dataset more precisely than other approaches and thus improves the discriminative abilities of object Re-ID models. Extensive experiments on both person and vehicle Re-ID datasets, such as Market-1501, DukeMTMC-ReID, CUHK03, and VeRi-776, show that the FSOR method has improved performance and outperforms state-of-the-art methods when the amount of labeled training data is small.
All Science Journal Classification (ASJC) codes
- 工程 (全部)