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近日，机器人研究中心博士生罗骜被模式识别权威期刊Pattern Recognition(IF: 7.196)录用了题为“EKENet: Efficient Knowledge Enhanced Network for Real-time Scene Parsing”的学术论文。

摘要：

Scene parsing is essential for many high-level AI applications, such as intelligent vehicles and traffic surveillance. In this work, we propose a highly efficient and powerful deep convolutional neural network, namely Efficient Knowledge Enhanced Network (EKENet), for parsing scenes in real-time. Unlike most existing approaches that compromise efficiency for the sake of high accuracy, EKENet achieves an ideal tradeoff between the two. Our EKENet is built upon a novel building block, namely Efficient Dual Abstraction (EDA) block, which employs an efficiently parallel convolution structure for extracting spatial features and modeling cross-channel correlations in a dual fashion. Additionally, a novel light-weight Encoding-Enhancing (EE) module is designed to enhance our EKENet, which can efficiently encode high-level knowledge extracted from top layers to guide the learning of low-level features from bottom layers. Extensive experiments on challenging benchmarks, Cityscapes and CamVid datasets, demonstrate that EKENet achieves the new state-of-the-art performance in terms of speed and accuracy tradeoff.