SpinNet: Spinning convolutional network for lane boundary detection

In this paper,we propose a simple but effective framework for lane boundary detection,called Spin Net.Considering that cars or pedestrians often occlude lane boundaries and that the local features of lane boundaries are not distinctive,therefore,analyzing and collecting global context information is crucial for lane boundary detection.To this end,we design a novel spinning convolution layer and a brand-new lane parameterization branch in our network to detect lane boundaries from a global perspective.To extract features in narrow strip-shaped fields,we adopt stripshaped convolutions with kernels which have 1×n or n×1 shape in the spinning convolution layer.To tackle the problem of that straight strip-shaped convolutions are only able to extract features in vertical or horizontal directions,we introduce the concept of feature map rotation to allow the convolutions to be applied in multiple directions so that more information can be collected concerning a whole lane boundary.Moreover,unlike most existing lane boundary detectors,which extract lane boundaries from segmentation masks,our lane boundary parameterization branch predicts a curve expression for the lane boundary for each pixel in the output feature map.And the network utilizes this information to predict the weights of the curve,to better form the final lane boundaries.Our framework is easy to implement and end-to-end trainable.Experiments show that our proposed Spin Net outperforms state-of-the-art methods.

Detecting human-object interaction with multi-level pairwise feature network

Human–object interaction(HOI)detection is crucial for human-centric image understanding which aims to infer human,action,object triplets within an image.Recent studies often exploit visual features and the spatial configuration of a human–object pair in order to learn the action linking the human and object in the pair.We argue that such a paradigm of pairwise feature extraction and action inference can be applied not only at the whole human and object instance level,but also at the part level at which a body part interacts with an object,and at the semantic level by considering the semantic label of an object along with human appearance and human–object spatial configuration,to infer the action.We thus propose a multi-level pairwise feature network(PFNet)for detecting human–object interactions.The network consists of three parallel streams to characterize HOI utilizing pairwise features at the above three levels;the three streams are finally fused to give the action prediction.Extensive experiments show that our proposed PFNet outperforms other state-of-the-art methods on the VCOCO dataset and achieves comparable results to the state-of-the-art on the HICO-DET dataset.

Key technological innovations in the construction of the main terminal of Beijing Daxing International Airport

Beijing Daxing International Airport,a major landmark project in China and a new driver for national development,will represent the direction for the development of civil aviation airports in China.The airport,situated in the junction of Nangezhuang,Daxing District,Beijing City and Gu’an County,Hebei Province,has seen its north terminal area and north terminal built in the first phase,covering a total construction area of about 1.4 million m2.The north terminal area is designed to have an annual throughput of 45 million passengers.