As a discriminative method of one-shot learning, Siamese deep network allows recognizing an object from a single exemplar with the same class label. However, it does not take the advantage of the underlying structure and relationship among a multitude of instances since it only relies on pairs of instances for training. In this paper, we propose a quadruplet deep network to examine the potential connections among the training instances, aiming to achieve a more powerful representation. We design four shared networks that receive multi-tuple of instances as inputs and are connected by a novel loss function consisting of pair-loss and triplet-loss. According to the similarity metric, we select the most similar and the most dissimilar instances as the positive and negative inputs of triplet loss from each multi-tuple. We show that this scheme improves the training performance and convergence speed. Furthermore, we introduce a new weighted pair loss for an additional acceleration of the convergence. We demonstrate promising results for model-free tracking-by-detection of objects from a single initial exemplar in the Visual Object Tracking benchmark.

本文提出了一种基于四元组深度网络的新方法以应对样本数据的潜在关联性，该方法在视觉物体跟踪中的性能优于其他模型并具有较快的处理速度。

基于一次学习的四元组网络的快速视觉目标追踪