  In the face of scarcity in detailed training annotations, the ability to
perform object localization tasks in real-time with weak-supervision is very
valuable. However, the computational cost of generating and evaluating region
proposals is heavy. We adapt the concept of Class Activation Maps (CAM) into
the very first weakly-supervised 'single-shot' detector that does not require
the use of region proposals. To facilitate this, we propose a novel global
pooling technique called Spatial Pyramid Averaged Max (SPAM) pooling for
training this CAM-based network for object extent localisation with only weak
image-level supervision. We show this global pooling layer possesses a near
ideal flow of gradients for extent localization, that offers a good trade-off
between the extremes of max and average pooling. Our approach only requires a
single network pass and uses a fast-backprojection technique, completely
omitting any region proposal steps. To the best of our knowledge, this is the
first approach to do so. Due to this, we are able to perform inference in
real-time at 35fps, which is an order of magnitude faster than all previous
weakly supervised object localization frameworks.
