  We develop a novel compositional generative model for zero- and few-shot
learning to recognize fine-grained classes with a few or no training samples.
Our key observation is that generating holistic features for fine-grained
classes fails to capture small attribute differences between classes.
Therefore, we propose a feature composition framework that learns to extract
attribute features from training samples and combines them to construct
fine-grained features for rare and unseen classes. Feature composition allows
us to not only selectively compose features of every class from only relevant
training samples, but also obtain diversity among composed features via
changing samples used for the composition. In addition, instead of building
holistic features for classes, we use our attribute features to form dense
representations capable of capturing fine-grained attribute details of classes.
We propose a training scheme that uses a discriminative model to construct
features that are subsequently used to train the model itself. Therefore, we
directly train the discriminative model on the composed features without
learning a separate generative model. We conduct experiments on four popular
datasets of DeepFashion, AWA2, CUB, and SUN, showing the effectiveness of our
method.
