ACE: Adapting to Changing Environments for Semantic Segmentation
ACE: Adapting to Changing Environments for Semantic Segmentation 论文地址：https://arxiv.org/abs/1904.06268 在实时的语义分割任务中，数据环境随着时间发生变化时或者是新的数据环境未被训练过时，当前的神经网络分类算法经常会遭遇到数据域的偏移从而造成性能下降，比如从白天的场景变换到了晚上。作者提出了一种叫做
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相关文章
- 1. Learning to Adapt Structured Output Space for Semantic Segmentation
- 2. Learning to Adapt Structured Output Space for Semantic Segmentation——CVPR2018
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- 4. CycleEmotionGAN: Emotional Semantic Consistency Preserved CycleGAN for Adapting Image Emotions
- 5. Context Encoding for Semantic Segmentation
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- 7. Understanding Convolution for Semantic Segmentation
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- 9. BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
- 10. Fully Convolutional Adaptation Networks for Semantic Segmentation