In this paper, we propose a new challenging task named as \textbf{partial multi-view few-shot learning}, which unifies two tasks, i.e. few-shot learning and partial multi-view learning, together. Different from the traditional few-shot learning, this task aims to solve the few-shot learning problem given the incomplete multi-view prior knowledge, which conforms more with the real-world applications. However, this brings about two difficulties within this task. First, the gaps among different views can be large and hard to reduce, especially with sample scarcity. Second, due to the incomplete view information, few-shot learning becomes more challenging than the traditional one. To deal with the above issues, we propose a new \textbf{Meta-alignment and Context Gated-aggregation Network} by equipping meta-alignment and context gated-aggregation with partial multi-view GNNs. Specifically, the meta-alignment effectively maps the features from different views into a more compact latent space, thereby reducing the view gaps. Moreover, the context gated-aggregation alleviates the view-missing influence by leveraging the cross-view context. Extensive experiments are conducted on the PIE and ORL dataset for evaluating our proposed method. By comparing with other few-shot learning methods, our method obtains the state-of-the-art performance especially with heavily-missing views.
翻译:在本文中,我们提出一个新的具有挑战性的任务,名为\ textbf{ 部分多视图的多镜头学习},它集中了两项任务,即少见的学习和部分多视图学习。与传统的少见学习不同,这项任务的目的是解决少见的学习问题,因为之前的多视图知识不完整,更符合现实世界的应用。然而,这在这项任务中带来了两个困难。第一,不同观点之间的差距可能很大,很难缩小,特别是抽样少见。第二,由于阅读信息不完整,少见的学习比传统的少见学习更具挑战性。为了处理上述问题,我们提出了一个新的\ textbf{Meta-Agrat-Agate-Gagation Net-Nation Net Network Net},通过部分多视图 GNNps 配置元组合和背景合并来解决少见的学习问题。具体来说,元组合有效地将不同观点的特征描绘成一个更加紧凑的隐蔽的空间,从而缩小了视觉差距。此外,背景的隔阂比一些。为了处理上述问题,背景,我们的拟议的实验方法比重评估了我们所建的数据库。