Single-level feature-to-feature forecasting with deformable convolutions on coarse abstract features from a segmentation backbone achieves state-of-the-art results for nine-timestep future semantic segmentation on Cityscapes validation.
Recurrent Flow-Guided Semantic Forecasting
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abstract
Understanding the world around us and making decisions about the future is a critical component to human intelligence. As autonomous systems continue to develop, their ability to reason about the future will be the key to their success. Semantic anticipation is a relatively under-explored area for which autonomous vehicles could take advantage of (e.g., forecasting pedestrian trajectories). Motivated by the need for real-time prediction in autonomous systems, we propose to decompose the challenging semantic forecasting task into two subtasks: current frame segmentation and future optical flow prediction. Through this decomposition, we built an efficient, effective, low overhead model with three main components: flow prediction network, feature-flow aggregation LSTM, and end-to-end learnable warp layer. Our proposed method achieves state-of-the-art accuracy on short-term and moving objects semantic forecasting while simultaneously reducing model parameters by up to 95% and increasing efficiency by greater than 40x.
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cs.CV 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Single Level Feature-to-Feature Forecasting with Deformable Convolutions
Single-level feature-to-feature forecasting with deformable convolutions on coarse abstract features from a segmentation backbone achieves state-of-the-art results for nine-timestep future semantic segmentation on Cityscapes validation.