Compositional Fine-Grained Low-Shot Learning

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.