Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation
This addresses the challenge of reducing annotation costs for semantic segmentation, though it is incremental as it builds on prior weakly-supervised methods.
The paper tackles the problem of weakly-supervised semantic segmentation using only image tags, which avoids expensive pixel-level annotations, and achieves state-of-the-art results by extracting accurate masks from pre-trained networks without external objectness modules.
Pixel-level annotations are expensive and time consuming to obtain. Hence, weak supervision using only image tags could have a significant impact in semantic segmentation. Recently, CNN-based methods have proposed to fine-tune pre-trained networks using image tags. Without additional information, this leads to poor localization accuracy. This problem, however, was alleviated by making use of objectness priors to generate foreground/background masks. Unfortunately these priors either require pixel-level annotations/bounding boxes, or still yield inaccurate object boundaries. Here, we propose a novel method to extract accurate masks from networks pre-trained for the task of object recognition, thus forgoing external objectness modules. We first show how foreground/background masks can be obtained from the activations of higher-level convolutional layers of a network. We then show how to obtain multi-class masks by the fusion of foreground/background ones with information extracted from a weakly-supervised localization network. Our experiments evidence that exploiting these masks in conjunction with a weakly-supervised training loss yields state-of-the-art tag-based weakly-supervised semantic segmentation results.