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Convolutional Scale Invariance for Semantic Segmentation (CROSBI ID 644339)

Prilog sa skupa u časopisu | izvorni znanstveni rad | međunarodna recenzija

Krešo, Ivan ; Čaušević, Denis ; Krapac, Josip ; Šegvić, Siniša Convolutional Scale Invariance for Semantic Segmentation // Lecture notes in computer science / Rosenhahn, Bodo, Andres, Bjoern (ur.). 2016. str. 64-75

Podaci o odgovornosti

Krešo, Ivan ; Čaušević, Denis ; Krapac, Josip ; Šegvić, Siniša

engleski

Convolutional Scale Invariance for Semantic Segmentation

We propose an effective technique to address large scale variation in images taken from a moving car by cross-breeding deep learning with stereo reconstruction. Our main contribution is a novel scale selection layer which extracts convolutional features at the scale which matches the corresponding reconstructed depth. The recovered scale-invariant representation disentangles appearance from scale and frees the pixel-level classifier from the need to learn the laws of the perspective. This results in improved segmentation results due to more efficient exploitation of representation capacity and training data. We perform experiments on two challenging stereoscopic datasets (KITTI and Cityscapes) and report competitive class-level IoU performance.

Convolutional networks, semantic segmentation

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Podaci o prilogu

64-75.

2016.

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objavljeno

978-3-319-45885-4

Podaci o matičnoj publikaciji

Lecture notes in computer science

Rosenhahn, Bodo, Andres, Bjoern

Hannover: Springer

0302-9743

Podaci o skupu

38th German Conference on Pattern Recognition GCPR 2016.

predavanje

12.09.2016-15.09.2016

Hannover, Njemačka

Povezanost rada

Računarstvo

Indeksiranost