# Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation Source: [Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:57:11.341Z Authors: Tianyi Zhang, Guosheng Lin, Jianfei Cai, Tong Shen, Chunhua Shen, Alex C. Kot Published: 2018-03-07T08:59:35.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on PASCAL VOC 2012 (test)](): Semantic segmentation; PASCAL VOC 2012 (test); mIoU: 60.1. - [Semantic Segmentation on PASCAL VOC 2012 (val)](): Semantic segmentation; PASCAL VOC 2012 (val); Mean IoU: 58.2. ## Original sources and implementation [Original paper]() [PDF]()