Dense Semantic Forecasting with Multi-Level Feature Warping

Anticipation of per-pixel semantics in a future unobserved frame is also known as dense semantic forecasting. State-of-the-art methods are based on single-level regression of a subsampled abstract representation of a recognition model. However, single-level regression cannot account for skip connections from the backbone to the upsampling path. We propose to address this shortcoming by warping shallow features from observed images with upsampled feature flow. Our goal is not straightforward, since warping with coarse feature flow introduces noise into the forecasted features. We therefore base our work on single-frame models that are more resistant to the noise in skip connections. To achieve this, we propose a training procedure that enables recognition models to operate reasonably well with or without skip connections. Validation experiments reveal interesting insights into the influence of particular skip connections on recognition accuracy. Our forecasting method delivers 70.2% mIoU 0.18 s into the future and 58.5% mIoU 0.54 s into the future. These experiments show 0.6 mIoU points of improved accuracy with respect to the baseline and reveal promising directions for future work.