Scalable logo detection by self co-learning

Hang Su, Shaogang Gong, Xiatian Zhu

Abstract: Highlights•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.