ARoad Segmentation Model Based on Mixture of the Convolutional Neural Network and the Transformer Network

Convolutional neural networks(CNN)based on U-shaped structures and skip connections play a pivotal role in various image segmentation tasks.Recently,Transformer starts to lead new trends in the image segmentation task.Transformer layer can construct the relationship between all pixels,and the two parties can complement each other well.On the basis of these characteristics,we try to combine Transformer pipeline and convolutional neural network pipeline to gain the advantages of both.The image is put into the U-shaped encoder-decoder architecture based on empirical combination of self-attention and convolution,in which skip connections are utilized for localglobal semantic feature learning.At the same time,the image is also put into the convolutional neural network architecture.The final segmentation result will be formed by Mix block which combines both.The mixture model of the convolutional neural network and the Transformer network for road segmentation(MCTNet)can achieve effective segmentation results on KITTI dataset and Unstructured Road Scene(URS)dataset built by ourselves.Codes,self-built datasets and trainable models will be available on https://github.com/xflxfl1992/MCTNet.

An Empirical Comparison on Multi-Target Regression Learning

Multi-target regression is concerned with the simultaneous prediction of multiple continuous target variables based on the same set of input variables.It has received relatively small attention from the Machine Learning community.However,multi-target regression exists in many real-world applications.In this paper we conduct extensive experiments to investigate the performance of three representative multi-target regression learning algorithms(i.e.Multi-Target Stacking(MTS),Random Linear Target Combination(RLTC),and Multi-Objective Random Forest(MORF)),comparing the baseline single-target learning.Our experimental results show that all three multi-target regression learning algorithms do improve the performance of the single-target learning.Among them,MTS performs the best,followed by RLTC,followed by MORF.However,the single-target learning sometimes still performs very well,even the best.This analysis sheds the light on multi-target regression learning and indicates that the single-target learning is a competitive baseline for multi-target regression learning on multi-target domains.