  Zero-shot learning (ZSL) aims to discriminate images from unseen classes by
exploiting relations to seen classes via their attribute-based descriptions.
Since attributes are often related to specific parts of objects, many recent
works focus on discovering discriminative regions. However, these methods
usually require additional complex part detection modules or attention
mechanisms. In this paper, 1) we show that common ZSL backbones (without
explicit attention nor part detection) can implicitly localize attributes, yet
this property is not exploited. 2) Exploiting it, we then propose SELAR, a
simple method that further encourages attribute localization, surprisingly
achieving very competitive generalized ZSL (GZSL) performance when compared
with more complex state-of-the-art methods. Our findings provide useful insight
for designing future GZSL methods, and SELAR provides an easy to implement yet
strong baseline.
