Acquiring sufficient ground-truth supervision to train deep visual models has
been a bottleneck over the years due to the data-hungry nature of deep
learning. This is exacerbated in some structured prediction tasks, such as
semantic segmentation, which requires pixel-level annotations. This work
addresses weakly supervised semantic segmentation (WSSS), with the goal of
bridging the gap between image-level annotations and pixel-level segmentation.
We formulate WSSS as a novel group-wise learning task that explicitly models
semantic dependencies in a group of images to estimate more reliable pseudo
ground-truths, which can be used for training more accurate segmentation
models. In particular, we devise a graph neural network (GNN) for group-wise
semantic mining, wherein input images are represented as graph nodes, and the
underlying relations between a pair of images are characterized by an efficient
co-attention mechanism. Moreover, in order to prevent the model from paying
excessive attention to common semantics only, we further propose a graph
dropout layer, encouraging the model to learn more accurate and complete object
responses. The whole network is end-to-end trainable by iterative message
passing, which propagates interaction cues over the images to progressively
improve the performance. We conduct experiments on the popular PASCAL VOC 2012
and COCO benchmarks, and our model yields state-of-the-art performance. Our
code is available at: this https URL

本研究提出一种基于图神经网络的弱监督语义分割方法，将图像表示为图的节点，使用组内注意力机制建立图中的关系，通过迭代信息传递的方式学习更准确、更完整的物体响应，并在多个基准数据集上取得了最先进的性能。