3 research outputs found
Imagination Based Sample Construction for Zero-Shot Learning
Zero-shot learning (ZSL) which aims to recognize unseen classes with no
labeled training sample, efficiently tackles the problem of missing labeled
data in image retrieval. Nowadays there are mainly two types of popular methods
for ZSL to recognize images of unseen classes: probabilistic reasoning and
feature projection. Different from these existing types of methods, we propose
a new method: sample construction to deal with the problem of ZSL. Our proposed
method, called Imagination Based Sample Construction (IBSC), innovatively
constructs image samples of target classes in feature space by mimicking human
associative cognition process. Based on an association between attribute and
feature, target samples are constructed from different parts of various
samples. Furthermore, dissimilarity representation is employed to select
high-quality constructed samples which are used as labeled data to train a
specific classifier for those unseen classes. In this way, zero-shot learning
is turned into a supervised learning problem. As far as we know, it is the
first work to construct samples for ZSL thus, our work is viewed as a baseline
for future sample construction methods. Experiments on four benchmark datasets
show the superiority of our proposed method.Comment: Accepted as a short paper in ACM SIGIR 201