  Thanks to the availability of powerful computing resources, big data and deep
learning algorithms, we have made great progress on computer vision in the last
few years. Computer vision systems begin to surpass humans in some tasks, such
as object recognition, object detection, face recognition and pose estimation.
Lots of computer vision algorithms have been deployed to real world
applications and started to improve our life quality. However, big data and
labels are not always available. Sometimes we only have very limited labeled
data, such as medical images which requires experts to label them. In this
paper, we study few shot image classification, in which we only have very few
labeled data. Machine learning with little data is a big challenge. To tackle
this challenge, we propose two methods and test their effectiveness thoroughly.
One method is to augment image features by mixing the style of these images.
The second method is applying spatial attention to explore the relations
between patches of images. We also find that domain shift is a critical issue
in few shot learning when the training domain and testing domain are different.
So we propose a more realistic cross-domain few-shot learning with unlabeled
data setting, in which some unlabeled data is available in the target domain.
We propose two methods in this setting. Our first method transfers the style
information of the unlabeled target dataset to the samples in the source
dataset and trains a model with stylized images and original images. Our second
method proposes a unified framework to fully utilize all the data. Both of our
methods surpass the baseline method by a large margin.
