3,367 research outputs found
Bringing Background into the Foreground: Making All Classes Equal in Weakly-supervised Video Semantic Segmentation
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. Recent years have seen great progress in
weakly-supervised semantic segmentation, whether from a single image or from
videos. However, most existing methods are designed to handle a single
background class. In practical applications, such as autonomous navigation, it
is often crucial to reason about multiple background classes. In this paper, we
introduce an approach to doing so by making use of classifier heatmaps. We then
develop a two-stream deep architecture that jointly leverages appearance and
motion, and design a loss based on our heatmaps to train it. Our experiments
demonstrate the benefits of our classifier heatmaps and of our two-stream
architecture on challenging urban scene datasets and on the YouTube-Objects
benchmark, where we obtain state-of-the-art results.Comment: 11 pages, 4 figures, 7 tables, Accepted in ICCV 201
Exploiting saliency for object segmentation from image level labels
There have been remarkable improvements in the semantic labelling task in the
recent years. However, the state of the art methods rely on large-scale
pixel-level annotations. This paper studies the problem of training a
pixel-wise semantic labeller network from image-level annotations of the
present object classes. Recently, it has been shown that high quality seeds
indicating discriminative object regions can be obtained from image-level
labels. Without additional information, obtaining the full extent of the object
is an inherently ill-posed problem due to co-occurrences. We propose using a
saliency model as additional information and hereby exploit prior knowledge on
the object extent and image statistics. We show how to combine both information
sources in order to recover 80% of the fully supervised performance - which is
the new state of the art in weakly supervised training for pixel-wise semantic
labelling. The code is available at https://goo.gl/KygSeb.Comment: CVPR 201
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