关键词: 弱监督, 特征提取, 语义分割, Transformer

Abstract: Weakly supervised semantic segmentation (WSSS) is a technique that utilizes simpler and more easily obtainable image level annotations (boxes, points, lines, and category information) to train classification models to obtain object segmentation regions and achieve pixel level prediction of images. Since mainstream segmentation algorithms still rely on a large amount of annotated data, pixel-by-pixel annotation is expensive, time-consuming, and labor-intensive. In order to alleviate this burden, people are increasingly paying attention to the efficient use of weakly labeled information to generate labels. In terms of weak supervision, there are two issues to focus on: first, the generated pseudo label images are not complete enough; second, the classification is not accurate enough. The vast majority of existing methods only consider the first point, while the paper proposes a feature dynamic storage scheme based on diffusion method, which not only ensures the integrity of labels but also takes into account category features. Specifically, this paper suppresses the most prominent regions to diffuse the activation regions of the activation map, and uses a refinement scheme of foreground background separation to remove redundant noise from the class activation map. Finally, the input feature dynamic library introduces semantic labels during the training phase to update each category in real-time. Compared with most existing weakly supervised semantic segmentation methods on the Pascal VOC 2012 dataset, the method proposed in this paper shows superiority. Compared with the new method using Transformers as the backbone network, this method is 2.5 percentage points and 2.9 percentage points higher on the validation and test sets, respectively.

Key words: weakly supervised, feature extraction, semantic segmentation, Transformer