  Image classification has been studied extensively but there has been limited
work in the direction of using non-conventional, external guidance other than
traditional image-label pairs to train such models. In this thesis we present a
set of methods to leverage information about the semantic hierarchy induced by
class labels. In the first part of the thesis, we inject label-hierarchy
knowledge to an arbitrary classifier and empirically show that availability of
such external semantic information in conjunction with the visual semantics
from images boosts overall performance. Taking a step further in this
direction, we model more explicitly the label-label and label-image
interactions by using order-preserving embedding-based models, prevalent in
natural language, and tailor them to the domain of computer vision to perform
image classification. Although, contrasting in nature, both the CNN-classifiers
injected with hierarchical information, and the embedding-based models
outperform a hierarchy-agnostic model on the newly presented, real-world ETH
Entomological Collection image dataset
https://www.research-collection.ethz.ch/handle/20.500.11850/365379.
