Although there has been significant research in egocentric action
recognition, most methods and tasks, including EPIC-KITCHENS, suppose a fixed
set of action classes. Fixed-set classification is useful for benchmarking
methods, but is often unrealistic in practical settings due to the
compositionality of actions, resulting in a functionally infinite-cardinality
label set. In this work, we explore generalization with an open set of classes
by unifying two popular approaches: few- and zero-shot generalization (the
latter which we reframe as cross-modal few-shot generalization). We propose a
new set of splits derived from the EPIC-KITCHENS dataset that allow evaluation
of open-set classification, and use these splits to show that adding a
metric-learning loss to the conventional direct-alignment baseline can improve
zero-shot classification by as much as 10%, while not sacrificing few-shot
performance.

本文通过将 few-shot generalization 和 zero-shot generalization 相结合，并在传统的直接对齐基线上添加度量学习损失，探索了面向 open-set 分类的通用化方法，并在基于 EPIC-KITCHENS 数据集的新分裂的基础上进行了评估，结果表明这种方法可以提高零样本分类的准确性高达 10％，同时不损失 few-shot 性能。

统一几个与零个拍摄的自我中心动作识别

Unifying Few- and Zero-Shot Egocentric Action Recognition

Few-shot image classification aims at training a model from only a few
examples for each of the "novel" classes. This paper proposes the idea of
associative alignment for leveraging part of the base data by aligning the
novel training instances to the closely related ones in the base training set.
This expands the size of the effective novel training set by adding extra
"related base" instances to the few novel ones, thereby allowing a constructive
fine-tuning. We propose two associative alignment strategies: 1) a
metric-learning loss for minimizing the distance between related base samples
and the centroid of novel instances in the feature space, and 2) a conditional
adversarial alignment loss based on the Wasserstein distance. Experiments on
four standard datasets and three backbones demonstrate that combining our
centroid-based alignment loss results in absolute accuracy improvements of
4.4%, 1.2%, and 6.2% in 5-shot learning over the state of the art for object
recognition, fine-grained classification, and cross-domain adaptation,
respectively.

本论文提出了关联对齐的思想，将新颖的训练实例与基础训练集中密切相关的样本进行对齐，从而扩大了有效新颖训练集的规模。研究表明采用我们提出的关联对齐策略和以质心为基础的关联损失，相较于现有技术，可以分别在目标识别、细粒度分类和跨领域适应中提高 5-shot 学习的绝对准确率 4.4％、1.2％和 6.2％。