This paper mainly studies one-example and few-example video person re-identification. A multi-branch network PAM that jointly learns local and global features is proposed. PAM has high accuracy, few parameters and converges fast, which is suitable for few-example person re-identification. We iteratively estimates labels for unlabeled samples, incorporates them into training sets, and trains a more robust network. We propose the static relative distance sampling(SRD) strategy based on the relative distance between classes. For the problem that SRD can not use all unlabeled samples, we propose adaptive relative distance sampling (ARD) strategy. For one-example setting, We get 89.78 , 56.13 rank-1 accuracy on PRID2011 and iLIDS-VID respectively, and 85.16 , 45.36 mAP on DukeMTMC and MARS respectively, which exceeds the previous methods by large margin.
In the first type, @cite_8 propose a framework for solving the problem of one-shot classification. They first build a fully convolutional siamese network based on verification loss, and then use this network to calculate the similarity between the image to be identified and other labeled samples. The image is then recognized as a sample of the category which the most similar labeled sample belongs to. @cite_1 propose matching network. During the training process, some samples are selected to form a support set and the remaining samples are used as training images. They construct different encoders for the support set and training pictures. The classfier's output is a weighted sum of the predicted values between the support set and the training images. During the test process, one-shot sample are used as support set to predict the category of new images. @cite_14 use meta-learning methods to learn multiple similar tasks, and build two encoders for the gallery and probe respectively. Based on these encoders, they get gallery images' embedding according to the characteristics of the remaining gallery images. They get probe images' embedding according to the characteristics of the gallery images. In this way they obtain a more discriminative feature representation.
@cite_14 @cite_1 @cite_8
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In this paper, we investigate the challenging task of person re-identification from a new perspective and propose an end-to-end attention-based architecture for few-shot re-identification through meta-learning. The motivation for this task lies in the fact that humans, can usually identify another person after just seeing that given person a few times (or even once) by attending to their memory. On the other hand, the unique nature of the person re-identification problem, i.e., only few examples exist per identity and new identities always appearing during testing, calls for a few shot learning architecture with the capacity of handling new identities. Hence, we frame the problem within a meta-learning setting, where a neural network based meta-learner is trained to optimize a learner i.e., an attention-based matching function. Another challenge of the person re-identification problem is the small inter-class difference between different identities and large intra-class difference of the same identity. In order to increase the discriminative power of the model, we propose a new attention-based feature encoding scheme that takes into account the critical intra-view and cross-view relationship of images. We refer to the proposed Attention-based Re-identification Met alearning model as ARM. Extensive evaluations demonstrate the advantages of the ARM as compared to the state-of-the-art on the challenging PRID2011, CUHK01, CUHK03 and Market1501 datasets. Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new concepts rapidly from little data. In this work, we employ ideas from metric learning based on deep neural features and from recent advances that augment neural networks with external memories. Our framework learns a network that maps a small labelled support set and an unlabelled example to its label, obviating the need for fine-tuning to adapt to new class types. We then define one-shot learning problems on vision (using Omniglot, ImageNet) and language tasks. Our algorithm improves one-shot accuracy on ImageNet from 87.6 to 93.2 and from 88.0 to 93.8 on Omniglot compared to competing approaches. We also demonstrate the usefulness of the same model on language modeling by introducing a one-shot task on the Penn Treebank.