WebLogo-2M: Scalable Logo Detection by Deep Learning from the Web

Abstract

Existing logo detection methods usually consider a small number of logo classes and limited images per class with a strong assumption of requiring tedious object bounding box annotations, therefore not scalable to real-world applications. In this work, we tackle these challenges by exploring the webly data learning principle without the need for exhaustive manual labelling. Specifically, we propose a novel incremental learning approach, called Scalable Logo Self-Training (SLST), capable of automatically self-discovering informative training images from noisy web data for progressively improving model capability. Moreover, we introduce a very large (1,867,177 images of 194 logo classes) logo dataset "WebLogo-2M" <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> by an automatic web data collection and processing method. Extensive comparative evaluations demonstrate the superiority of the proposed SLST method over state-of-the-art strongly and weakly supervised detection models and contemporary webly data learning alternatives.