Single Level Feature-to-Feature Forecasting with Deformable Convolutions (CROSBI ID 686446)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Šarić, Josip ; Oršić, Marin ; Antunović, Tonći ; Vražić, Sacha ; Šegvić, Siniša
engleski
Single Level Feature-to-Feature Forecasting with Deformable Convolutions
Future anticipation is of vital importance in autonomous driving and other decision-making systems. We present a method to anticipate semantic segmentation of future frames in driving scenarios based on feature-to-feature forecasting. Our method is based on a semantic segmentation model without lateral connections within the upsampling path. Such design ensures that the forecasting addresses only the most abstract features on a very coarse resolution. We further propose to express feature-to-feature forecasting with deformable convolutions. This increases the modelling power due to being able to represent different motion patterns within a single feature map. Experiments show that our models with deformable convolutions outperform their regular and dilated counterparts while minimally increasing the number of parameters. Our method achieves state of the art performance on the Cityscapes validation set when forecasting nine timesteps into the future.
computer vision
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Podaci o prilogu
189-202.
2019.
objavljeno
10.1007/978-3-030-33676-9_13
Podaci o matičnoj publikaciji
Fink, Gernot A. ; Frintrop, Simone ; Jiang, Xiaoyi
Dortmund: Springer
Podaci o skupu
41th German Conference on Pattern Recognition (GCPR 2019)
predavanje
10.09.2019-13.09.2019
Dortmund, Njemačka