  This work focuses on the semantic relations between scenes and objects for
visual object recognition. Semantic knowledge can be a powerful source of
information especially in scenarios with few or no annotated training samples.
These scenarios are referred to as zero-shot or few-shot recognition and often
build on visual attributes. Here, instead of relying on various visual
attributes, a more direct way is pursued: after recognizing the scene that is
depicted in an image, semantic relations between scenes and objects are used
for predicting the presence of objects in an unsupervised manner. Most
importantly, relations between scenes and objects can easily be obtained from
external sources such as large scale text corpora from the web and, therefore,
do not require tremendous manual labeling efforts. It will be shown that in
cluttered scenes, where visual recognition is difficult, scene knowledge is an
important cue for predicting objects.
