Abstract
•The literature lacks large-scale logo detection test benchmarks due to rather expensive data selection and label annotation.•We contribute a large-scale dataset collected automatically for scalable logo detection.•We present a scalable logo detection solution characterised by joint co-learning and self-learning in a unified framework, without the tedious need for manually labelling any training data.
Existing logo detection methods usually consider a small number of logo classes, limited images per class and assume fine-gained object bounding box annotations. This limits their scalability to real-world dynamic applications. In this work, we tackle these challenges by exploring a web data learning principle without the need for exhaustive manual labelling. Specifically, we propose a novel incremental learning approach, called Scalable Logo Self-co-Learning (SL2), capable of automatically self-discovering informative training images from noisy web data for progressively improving model capability in a cross-model co-learning manner. Moreover, we introduce a very large (2,190,757 images of 194 logo classes) logo dataset “WebLogo-2M” by designing an automatic data collection and processing method. Extensive comparative evaluations demonstrate the superiority of SL2 over the state-of-the-art strongly and weakly supervised detection models and contemporary web data learning approaches.