  Deep neural networks deliver state-of-the-art visual recognition, but they
rely on large datasets, which are time-consuming to annotate. These datasets
are typically annotated in two stages: (1) determining the presence of object
classes at the image level and (2) marking the spatial extent for all objects
of these classes. In this work we use speech, together with mouse inputs, to
speed up this process. We first improve stage one, by letting annotators
indicate object class presence via speech. We then combine the two stages:
annotators draw an object bounding box via the mouse and simultaneously provide
its class label via speech. Using speech has distinct advantages over relying
on mouse inputs alone. First, it is fast and allows for direct access to the
class name, by simply saying it. Second, annotators can simultaneously speak
and mark an object location. Finally, speech-based interfaces can be kept
extremely simple, hence using them requires less mouse movement compared to
existing approaches. Through extensive experiments on the COCO and ILSVRC
datasets we show that our approach yields high-quality annotations at
significant speed gains. Stage one takes 2.3x - 14.9x less annotation time than
existing methods based on a hierarchical organization of the classes to be
annotated. Moreover, when combining the two stages, we find that object class
labels come for free: annotating them at the same time as bounding boxes has
zero additional cost. On COCO, this makes the overall process 1.9x faster than
the two-stage approach.
