The KITTI Vision Benchmark Suite

At present, most semantic segmentation models rely on the excellent feature extraction capabilities of a deep learning network structure. Although these models can achieve excellent performance on multiple datasets, ways of refining the target main body segmentation and overcoming the performance limitation of deep learning networks are still a research focus. We discovered a pan-class intrinsic relevance phenomenon among targets that can link the targets cross-class. This cross-class strategy is different from the latest semantic segmentation model via context where targets are divided into an intra-class and inter-class. This model proposes a model for refining the target main body segmentation using multi-target pan-class intrinsic relevance. The main contributions of the proposed model can be summarized as follows: a) The multi-target pan-class intrinsic relevance prior knowledge establishment (RPK-Est) module builds the prior knowledge of the intrinsic relevance to lay the foundat

The adaptive learning rate could be automatically set according to the number of training iterations to achieve the best training performance. The value was fixed to 0.7 ,which avoids the training divergence directly.

@InProceedings{Yingfeng Cai_2021_TIP,
author = {Yingfeng Cai, Lei Dai and Hai Wang, Zhixiong Li},
title = {Multi-Target Pan-Class Intrinsic Relevance Driven Model for Improving Semantic Segmentation in Autonomous Driving},
booktitle = {IEEE Transactions on Image Processing (TIP)},
month = {November},
year = {2021}
}