  Class Activation Mapping (CAM) methods have recently gained much attention
for weakly-supervised object localization (WSOL) tasks. They allow for CNN
visualization and interpretation without training on fully annotated image
datasets. CAM methods are typically integrated within off-the-shelf CNN
backbones, such as ResNet50. Due to convolution and pooling operations, these
backbones yield low resolution CAMs with a down-scaling factor of up to 32,
contributing to inaccurate localizations. Interpolation is required to restore
full size CAMs, yet it does not consider the statistical properties of objects,
such as color and texture, leading to activations with inconsistent boundaries,
and inaccurate localizations. As an alternative, we introduce a generic method
for parametric upscaling of CAMs that allows constructing accurate full
resolution CAMs (F-CAMs). In particular, we propose a trainable decoding
architecture that can be connected to any CNN classifier to produce highly
accurate CAM localizations. Given an original low resolution CAM, foreground
and background pixels are randomly sampled to fine-tune the decoder. Additional
priors such as image statistics and size constraints are also considered to
expand and refine object boundaries. Extensive experiments, over three CNN
backbones and six WSOL baselines on the CUB-200-2011 and OpenImages datasets,
indicate that our F-CAM method yields a significant improvement in CAM
localization accuracy. F-CAM performance is competitive with state-of-art WSOL
methods, yet it requires fewer computations during inference.
