Naslov
Single Level Feature-to-Feature Forecasting with Deformable Convolutions

Autori
Šarić, Josip ; Oršić, Marin ; Antunović, Tonći ; Vražić, Sacha ; Šegvić, Siniša

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Lecture Notes on Computer Science, vol 11824 / Fink, Gernot A. ; Frintrop, Simone ; Jiang, Xiaoyi - Dortmund : Springer, 2019, 189-202

Skup
41th German Conference on Pattern Recognition (GCPR 2019)

Mjesto i datum
Dortmund, Njemačka, 10.09.2019. - 13.09.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
computer vision

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

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