  Training a neural network model for recognizing multiple labels associated
with an image, including identifying unseen labels, is challenging, especially
for images that portray numerous semantically diverse labels. As challenging as
this task is, it is an essential task to tackle since it represents many
real-world cases, such as image retrieval of natural images. We argue that
using a single embedding vector to represent an image, as commonly practiced,
is not sufficient to rank both relevant seen and unseen labels accurately. This
study introduces an end-to-end model training for multi-label zero-shot
learning that supports semantic diversity of the images and labels. We propose
to use an embedding matrix having principal embedding vectors trained using a
tailored loss function. In addition, during training, we suggest up-weighting
in the loss function image samples presenting higher semantic diversity to
encourage the diversity of the embedding matrix. Extensive experiments show
that our proposed method improves the zero-shot model's quality in tag-based
image retrieval achieving SoTA results on several common datasets (NUS-Wide,
COCO, Open Images).
