Few-shot action recognition aims to recognize action classes with few training samples. Most existing methods adopt a meta-learning approach with episodic training. In each episode, the few samples in a meta-training task are split into support and query sets. The former is used to build a classifier, which is then evaluated on the latter using a query-centered loss for model updating. There are however two major limitations: lack of data efficiency due to the query-centered only loss design and inability to deal with the support set outlying samples and inter-class distribution overlapping problems. In this paper, we overcome both limitations by proposing a new Prototype-centered Attentive Learning (PAL) model composed of two novel components. First, a prototype-centered contrastive learning loss is introduced to complement the conventional query-centered learning objective, in order to make full use of the limited training samples in each episode. Second, PAL further integrates a hybrid attentive learning mechanism that can minimize the negative impacts of outliers and promote class separation. Extensive experiments on four standard few-shot action benchmarks show that our method clearly outperforms previous state-of-the-art methods, with the improvement particularly significant (10+\%) on the most challenging fine-grained action recognition benchmark.
翻译:少见的行动识别旨在承认行动课,少有培训样本。大多数现有方法都采用以偶发培训方式的元学习方法。在每一例中,元培训任务中的少数样本被分为支助和查询组。前者用来建立一个分类器,然后用以查询为中心的损失来评估后者,然后用以查询为中心的损失来更新模型。然而,有两个主要的局限性:由于以查询为中心的唯一损失设计导致数据效率低下,以及由于无法处理标出样本的支持和阶级间分配重叠问题。在本文中,我们通过提出由两个新组成部分组成的新的原型强化学习(PAL)模式克服了这两种限制。首先,引入了原型对比学习损失,以补充常规的以查询为中心的学习目标,以便在每一例中充分利用有限的培训样本。第二,PAL进一步整合了混合的认真学习机制,可以最大限度地减少外延者的消极影响,促进阶级分离。在四种标准几发行动基准上进行的广泛实验表明,我们的方法明显超越了以前最具有挑战性的行动改进(10个)的先进方法。