Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation
This work addresses the problem of reducing annotation costs for semantic segmentation, which is incremental as it builds on existing methods for generating pseudo-annotations.
The paper tackles weakly supervised semantic segmentation using image labels by proposing a decoupled spatial neural attention network to generate high-quality pseudo-annotations, achieving state-of-the-art results.
Weakly supervised semantic segmentation receives much research attention since it alleviates the need to obtain a large amount of dense pixel-wise ground-truth annotations for the training images. Compared with other forms of weak supervision, image labels are quite efficient to obtain. In our work, we focus on the weakly supervised semantic segmentation with image label annotations. Recent progress for this task has been largely dependent on the quality of generated pseudo-annotations. In this work, inspired by spatial neural-attention for image captioning, we propose a decoupled spatial neural attention network for generating pseudo-annotations. Our decoupled attention structure could simultaneously identify the object regions and localize the discriminative parts which generates high-quality pseudo-annotations in one forward path. The generated pseudo-annotations lead to the segmentation results which achieve the state-of-the-art in weakly-supervised semantic segmentation.