Contemporary deep learning techniques have made image recognition a reasonably reliable technology. However training effective photo classifiers typically takes numerous examples which limits image recognition's scalability and applicability to scenarios where images may not be available. This has motivated investigation into zero-shot learning, which addresses the issue via knowledge transfer from other modalities such as text. In this paper we investigate an alternative approach of synthesizing image classifiers: almost directly from a user's imagination, via freehand sketch. This approach doesn't require the category to be nameable or describable via attributes as per zero-shot learning. We achieve this via training a model regression network to map from free-hand sketch space to the space of photo classifiers. It turns out that this mapping can be learned in a category-agnostic way, allowing photo classifiers for new categories to be synthesized by user with no need for annotated training photos. We also demonstrate that this modality of classifier generation can also be used to enhance the granularity of an existing photo classifier, or as a complement to name-based zero-shot learning.
One of the main challenges in learning fine-grained visual categories is gathering training images. Recent work in Zero-Shot Learning (ZSL) circumvents this challenge by describing categories via attributes or text. However, not all visual concepts, e.g., two people dancing, are easily amenable to such descriptions. In this paper, we propose a new modality for ZSL using visual abstraction to learn difficult-to-describe concepts. Specifically, we explore concepts related to people and their interactions with others. Our proposed modality allows one to provide training data by manipulating abstract visualizations, e.g., one can illustrate interactions between two clipart people by manipulating each person’s pose, expression, gaze, and gender. The feasibility of our approach is shown on a human pose dataset and a new dataset containing complex interactions between two people, where we outperform several baselines. To better match across the two domains, we learn an explicit mapping between the abstract and real worlds.