  Multi-label image recognition is a challenging computer vision task of
practical use. Progresses in this area, however, are often characterized by
complicated methods, heavy computations, and lack of intuitive explanations. To
effectively capture different spatial regions occupied by objects from
different categories, we propose an embarrassingly simple module, named
class-specific residual attention (CSRA). CSRA generates class-specific
features for every category by proposing a simple spatial attention score, and
then combines it with the class-agnostic average pooling feature. CSRA achieves
state-of-the-art results on multilabel recognition, and at the same time is
much simpler than them. Furthermore, with only 4 lines of code, CSRA also leads
to consistent improvement across many diverse pretrained models and datasets
without any extra training. CSRA is both easy to implement and light in
computations, which also enjoys intuitive explanations and visualizations.
