Sketch-a-Classifier: Sketch-based Photo Classifier Generation
This enables scalable image recognition for scenarios where images are unavailable or categories are not easily nameable, though it is an incremental improvement over zero-shot learning methods.
The paper tackles the problem of training image classifiers without requiring numerous annotated photos by synthesizing classifiers directly from free-hand sketches, achieving a 45.2% top-1 accuracy on a benchmark dataset.
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 free-hand 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.