Semantic segmentation is one of the important technologies in autonomous driving, and ensuring its real-time and high performance is of utmost importance for the safety of pedestrians and passengers. To improve its performance using deep neural networks that operate in real-time, the authors propose a simple and efficient method called ADFNet using accumulated decoder features, ADFNet operates by only using the decoder information without skip connections between the encoder and decoder. They demonstrate that the performance of ADFNet is superior to that of the state-of-the-art methods, including that of the baseline network on the cityscapes dataset. Further, they analyse the results obtained via ADFNet using class activation maps and RGB representations for image segmentation results.

中文翻译：

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ADFNet：用于实时语义分段的累积解码器功能

语义分割是自动驾驶中的重要技术之一，确保其实时性和高性能对于行人和乘客的安全至关重要。为了使用实时运行的深度神经网络提高其性能，作者提出了一种简单有效的方法，即使用累积的解码器功能进行ADFNet，ADFNet仅通过使用解码器信息进行操作，而不会在编码器和解码器之间建立跳过连接。他们证明，ADFNet的性能优于最新方法，包括城市景观数据集上基准网络的性能。此外，他们使用类激活图和RGB表示法对通过ADFNet获得的结果进行了图像分割结果分析。