Human Parsing Model Combined with Regional Sampling and Inter-class Loss

Abstract

Human parsing is a fine-grained level semantic segmentation task.The refinement of annotated categories in the human parsing dataset makes the dataset follow a long-tailed distribution and improves the difficulty of identifying similar categories.Balanced sampling is an efficient way to solve long-tailed distribution problem,but it’s difficult to achieve balanced sampling of the labeled object in human parsing.On the other hand,the fine-grained annotation will make the model misjudge similar categories.In response to these problems,a human parsing model combined with regional sampling and inter-class loss is proposed.The model consists of the semantic segmentation network,regionally balanced sampling module(RBSM),and inter-class loss module(ILM).Firstly,the images are parsed by the semantic segmentation network.Next,the parsing results and the ground truth labels are sampled by regionally balanced sampling module.Then the sampled parsing results and sampled ground truth labels are utilized to calculate the master loss.Meanwhile,the inter-class loss between the heatmap features coming from the semantic segmentation network and ground truth labels are calculated in the inter-class loss module,and the master loss and the inter-class loss are optimized at the same time to get a more accurate model.Experimental results based on the MHPv2.0 dataset show that the mIoU of the proposed model improves by more than 1.3% without changing the structure of the semantic segmentation network.The algorithm effectively reduces the impact of the long tail distribution problem and similarity among categories.

Keywords

• regional sampling|inter-loss|long-tailed distribution|human parsing|semantic segmentation