  One of the challenges of logo recognition lies in the diversity of forms,
such as symbols, texts or a combination of both; further, logos tend to be
extremely concise in design while similar in appearance, suggesting the
difficulty of learning discriminative representations. To investigate the
variety and representation of logo, we introduced Makeup216, the largest and
most complex logo dataset in the field of makeup, captured from the real world.
It comprises of 216 logos and 157 brands, including 10,019 images and 37,018
annotated logo objects. In addition, we found that the marginal background
around the pure logo can provide a important context information and proposed
an adversarial attention representation framework (AAR) to attend on the logo
subject and auxiliary marginal background separately, which can be combined for
better representation. Our proposed framework achieved competitive results on
Makeup216 and another large-scale open logo dataset, which could provide fresh
thinking for logo recognition. The dataset of Makeup216 and the code of the
proposed framework will be released soon.
