Context Contrasted Feature and Gated Multi-scale Aggregation
for Scene Segmentation
Abstract
Scene segmentation is a challenging task as it need
label every pixel in the image. It is crucial to exploit
discriminative context and aggregate multi-scale features
to achieve better segmentation. In this paper, we first
propose a novel context contrasted local feature that not
only leverages the informative context but also spotlights
the local information in contrast to the context. The proposed context contrasted local feature greatly improves the
parsing performance, especially for inconspicuous objects
and background stuff. Furthermore, we propose a scheme of
gated sum to selectively aggregate multi-scale features for
each spatial position. The gates in this scheme control the
information flow of different scale features. Their values are
generated from the testing image by the proposed network
learnt from the training data so that they are adaptive
not only to the training data, but also to the specific
testing image. Without bells and whistles, the proposed
approach achieves the state-of-the-arts consistently on the
three popular scene segmentation datasets, Pascal Context,
SUN-RGBD and COCO Stuff