  Precise segmentation of objects is an important problem in tasks like
class-agnostic object proposal generation or instance segmentation. Deep
learning-based systems usually generate segmentations of objects based on
coarse feature maps, due to the inherent downsampling in CNNs. This leads to
segmentation boundaries not adhering well to the object boundaries in the
image. To tackle this problem, we introduce a new superpixel-based refinement
approach on top of the state-of-the-art object proposal system AttentionMask.
The refinement utilizes superpixel pooling for feature extraction and a novel
superpixel classifier to determine if a high precision superpixel belongs to an
object or not. Our experiments show an improvement of up to 26.0% in terms of
average recall compared to original AttentionMask. Furthermore, qualitative and
quantitative analyses of the segmentations reveal significant improvements in
terms of boundary adherence for the proposed refinement compared to various
deep learning-based state-of-the-art object proposal generation systems.
