Object class labelling is the task of annotating images with labels on the
presence or absence of objects from a given class vocabulary. Simply asking one
yes/no question per class, however, has a cost that is linear in the vocabulary
size and is thus inefficient for large vocabularies. Modern approaches rely on
a hierarchical organization of the vocabulary to reduce annotation time, but
remain expensive (several minutes per image for the 200 classes in ILSVRC).
Instead, we propose a new interface where classes are annotated via speech.
Speaking is fast and allows for direct access to the class name, without
searching through a list or hierarchy. As additional advantages, annotators can
simultaneously speak and scan the image for objects, the interface can be kept
extremely simple, and using it requires less mouse movement. As annotators
using our interface should only say words from a given class vocabulary, we
propose a dedicated task that trains them to do so. Through experiments on COCO
and ILSVRC, we show our method yields high-quality annotations at 2.3x - 14.9x
less annotation time than existing methods.

该研究提出了一种基于语音的标注界面来减少目标类别标注所需的时间，并在 COCO 和 ILSVRC 等数据集上实验证明了该方法相较于现有方法可以在更短时间内获得高质量的标注。