Supplementary materials DADA: Depth-aware Domain Adaptation in Semantic Segmentation Tuan-Hung Vu 1 Himalaya Jain 1 Maxime Bucher 1 Matthieu Cord 1,2 Patrick P´ erez 1 1 valeo.ai, Paris, France 2 Sorbonne University, Paris, France (a) Input image (b) GT (c) AdvEnt (d) DADA Additional qualitative results in the SYNTHIA→Cityscapes (16 classes) set-up. The four columns plot (a) RGB input images, (b) ground-truths, (c) AdvEnt baseline outputs and (d) DADA predictions. DADA shows good performance on ‘bus’, ‘car’, ‘bicycle’ classes. Best viewed in color. 1
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Supplementary materialsDADA: Depth-aware Domain Adaptation in Semantic Segmentation
Tuan-Hung Vu1 Himalaya Jain1 Maxime Bucher1 Matthieu Cord1,2 Patrick Perez1
1valeo.ai, Paris, France 2Sorbonne University, Paris, France
(a) Input image (b) GT (c) AdvEnt (d) DADA
Additional qualitative results in the SYNTHIA→Cityscapes (16 classes) set-up. The four columns plot (a) RGB input images, (b)ground-truths, (c) AdvEnt baseline outputs and (d) DADA predictions. DADA shows good performance on ‘bus’, ‘car’, ‘bicycle’ classes.Best viewed in color.
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