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Pixel-level annotations are expensive and time-consuming to obtain. Hence,weak supervision using only image tags could have a significant impact insemantic segmentation. Recent years have seen great progress inweakly-supervised semantic segmentation, whether from a single image or fromvideos. However, most existing methods are designed to handle a singlebackground class. In practical applications, such as autonomous navigation, itis often crucial to reason about multiple background classes. In this paper, weintroduce an approach to doing so by making use of classifier heatmaps. We thendevelop a two-stream deep architecture that jointly leverages appearance andmotion, and design a loss based on our heatmaps to train it. Our experimentsdemonstrate the benefits of our classifier heatmaps and of our two-streamarchitecture on challenging urban scene datasets and on the YouTube-Objectsbenchmark, where we obtain state-of-the-art results.

Bringing Background into the Foreground: Making All Classes Equal in Weakly-supervised Video Semantic Segmentation