  Cartoons and animation domain videos have very different characteristics
compared to real-life images and videos. In addition, this domain carries a
large variability in styles. Current computer vision and deep-learning
solutions often fail on animated content because they were trained on natural
images. In this paper we present a method to refine a semantic representation
suitable for specific animated content. We first train a neural network on a
large-scale set of animation videos and use the mapping to deep features as an
embedding space. Next, we use self-supervision to refine the representation for
any specific animation style by gathering many examples of animated characters
in this style, using a multi-object tracking. These examples are used to define
triplets for contrastive loss training. The refined semantic space allows
better clustering of animated characters even when they have diverse
manifestations. Using this space we can build dictionaries of characters in an
animation videos, and define specialized classifiers for specific stylistic
content (e.g., characters in a specific animation series) with very little user
effort. These classifiers are the basis for automatically labeling characters
in animation videos. We present results on a collection of characters in a
variety of animation styles.
