guanfuchen
guanfuchen
related paper |摘要| |---| |In this paper, we address the scene segmentation task by capturing rich contextual dependencies based on the selfattention mechanism. Unlike previous works that capture contexts by...
related paper |摘要| |---| |A Pyramid Attention Network(PAN) is proposed to exploit the impact of global contextual information in semantic segmentation. Different from most existing works, we combine attention mechanism...
related paper |摘要| |---| |State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for...
related paper |摘要| |---| |Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification problems such...
related paper |摘要| |---| |We consider an important task of effective and efficient semantic image segmentation. In particular, we adapt a powerful semantic segmentation architecture, called RefineNet [46], into the...
related paper |摘要| |---| |The ability to perform pixel-wise semantic segmentation in real-time is of paramount importance in mobile applications. Recent deep neural networks aimed at this task have the...
related paper |摘要| |---| |The demand of applying semantic segmentation model on mobile devices has been increasing rapidly. Current state-of-the-art networks have enormous amount of parameters hence unsuitable for mobile...
related paper |摘要| |---| |Convolutional neural networks (CNNs) have achieved great successes in many computer vision problems. Unlike existing works that designed CNN architectures to improve performance on a single...
Here is some paper about semantic segmentation, and I list some performance for comparing. I will check the performance again. 
use the code for training CamVid, here is the sample result  when the arch segnet-vgg16 change to segnet-vgg19, the detail about pedestrian is more clear. 