Scalable Object Detection for Stylized Objects
This addresses the challenge of labor-intensive annotations and scalability for large-scale object detection in domains like logo recognition, though it is incremental as it builds on existing CNN and retrieval methods.
The paper tackles the problem of scaling object detection to tens of thousands or more unique classes, particularly for stylized objects like brand logos, by proposing a two-layer method that combines a CNN for region proposals with an image index for retrieval, achieving state-of-the-art quality on tasks such as logo recognition.
Following recent breakthroughs in convolutional neural networks and monolithic model architectures, state-of-the-art object detection models can reliably and accurately scale into the realm of up to thousands of classes. Things quickly break down, however, when scaling into the tens of thousands, or, eventually, to millions or billions of unique objects. Further, bounding box-trained end-to-end models require extensive training data. Even though - with some tricks using hierarchies - one can sometimes scale up to thousands of classes, the labor requirements for clean image annotations quickly get out of control. In this paper, we present a two-layer object detection method for brand logos and other stylized objects for which prototypical images exist. It can scale to large numbers of unique classes. Our first layer is a CNN from the Single Shot Multibox Detector family of models that learns to propose regions where some stylized object is likely to appear. The contents of a proposed bounding box is then run against an image index that is targeted for the retrieval task at hand. The proposed architecture scales to a large number of object classes, allows to continously add new classes without retraining, and exhibits state-of-the-art quality on a stylized object detection task such as logo recognition.