Titlebook: Artificial Intelligence; Second CAAI Internat Lu Fang,Daniel Povey,Ruiping Wang Conference proceedings 2022 The Editor(s) (if applicable) a

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厭惡

fetter

認識

micturition

infringe

Unsupervised Domain Adaptation for?Semantic Segmentation with?Global and?Local Consistencyfirst constrain global style consistency through a generative adversarial network to acquire real-like latent domain images. Then we enhance local content consistency based on pixel-wise entropy minimization. Experimental results show that our method has superiority over other competitive methods on GTA5 . Cityscapes.

小故事

MAG

燦爛

Windows Presentation Foundation UI,bidirectional fused images for training. BSAM ensures the correct scene layout, facilitating the model to adapt to the different scenario characteristics. Extensive experiments on two benchmarks (GTA5 to Cityscapes and SYNTHIA to Cityscapes) demonstrate that BSAM achieves state-of-the-art performance.

遭受

Authentication and Authorization,scriminator is utilized to adversarially regularize the future trajectory predictions to be in line with the observed trajectories. Extensive experiments demonstrate the effectiveness of our method on domain adaptation for pedestrian trajectory prediction.