Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation
This work addresses the challenge of reducing annotation costs for semantic segmentation, offering a practical solution for applications in computer vision, though it is incremental by building on prior CNN-based 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 performance by extracting accurate foreground/background masks from a pre-trained network 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 training pixel-level annotations/bounding boxes, or still yield inaccurate object boundaries. Here, we propose a novel method to extract markedly more accurate masks from the pre-trained network itself, forgoing external objectness modules. This is accomplished using the activations of the higher-level convolutional layers, smoothed by a dense CRF. We demonstrate that our method, based on these masks and a weakly-supervised loss, outperforms the state-of-the-art tag-based weakly-supervised semantic segmentation techniques. Furthermore, we introduce a new form of inexpensive weak supervision yielding an additional accuracy boost.