遭遇
发表于 2025-3-25 03:49:37
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厌恶
发表于 2025-3-25 09:28:35
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fetter
发表于 2025-3-25 15:10:52
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认识
发表于 2025-3-25 16:15:23
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micturition
发表于 2025-3-25 19:58:42
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infringe
发表于 2025-3-26 03:41:11
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
发表于 2025-3-26 05:33:24
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MAG
发表于 2025-3-26 10:31:58
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灿烂
发表于 2025-3-26 14:53:49
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
发表于 2025-3-26 18:21:51
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.