Predicting Deeper into the Future of Semantic Segmentation

Pauline Luc 1, 2 Natalia Neverova 1 Camille Couprie 1 Jakob Verbeek 2 Yann Lecun 3
1 FAIR - Facebook AI Research [Paris]
2 Thoth - Apprentissage de modèles à partir de données massives
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann
3 NYU - New York University [New York]
Abstract : The ability to predict and therefore to anticipate the future is an important attribute of intelligence. It is also of utmost importance in real-time systems, e.g. in robotics or autonomous driving, which depend on visual scene understanding for decision making. While prediction of the raw RGB pixel values in future video frames has been studied in previous work, here we introduce the novel task of predicting semantic segmentations of future frames. Given a sequence of video frames, our goal is to predict segmentation maps of not yet observed video frames that lie up to a second or further in the future. We develop an autoregressive convolutional neural network that learns to iteratively generate multiple frames. Our results on the Cityscapes dataset show that directly predicting future segmentations is substantially better than predicting and then segmenting future RGB frames. Prediction results up to half a second in the future are visually convincing and are much more accurate than those of a baseline based on warping semantic segmentations using optical flow.
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https://hal.inria.fr/hal-01494296
Contributor : Pauline Luc <>
Submitted on : Monday, August 21, 2017 - 1:37:02 PM
Last modification on : Wednesday, August 14, 2019 - 10:46:03 AM

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Pauline Luc, Natalia Neverova, Camille Couprie, Jakob Verbeek, Yann Lecun. Predicting Deeper into the Future of Semantic Segmentation. ICCV 2017 - International Conference on Computer Vision, Oct 2017, Venise, Italy. pp.648-657, ⟨10.1109/ICCV.2017.77⟩. ⟨hal-01494296v2⟩

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