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GaoYiao 846ce6deac | 2 years ago | |
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TrainOneStepMyself.py | 2 years ago | |
dataset_test.py | 2 years ago | |
dataset_train.py | 2 years ago | |
loss.py | 2 years ago | |
model.py | 2 years ago | |
resnet50.py | 2 years ago |
RAS是一种用于显著性目标检测的网络。首先,该模型提出了两种初始预测策略,一种是新设计的多尺度上下文模块,另一种是结合手工制作的显著性先验。 其次,利用残差学习逐步细化,只学习各边输出中的残差,这可以在较少的卷积参数下实现,从而获得较高的紧凑性和效率。 最后,进一步设计了一种新的自上而下的反向注意块来指导上述侧输出残差学习。具体来说,利用当前预测的显著区域来去除其侧输出特征,从而可以有效地从这些未删除的区域中学习到缺失的目标部分和细节,从而实现检测更完整和更高的精度。
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