STI: Enhancing Semisupervised Semantic Segmentation via Image Search and Advanced Pooling Strategies

Self-training semisupervised semantic segmentation has emerged as a significant approach, yet the quality of pseudo labels remains a critical factor influencing its performance. This study aims to enhance the quality of pseudo labels in self-training semisupervised semantic segmentation by introducing a novel image search module. This module augments the utilization of labeled data, thereby improving the generation of pseudo labels. Additionally, we design an adaptive soft pooling mechanism within the pooling feature map of the image search module to better capture and model intricate global feature dependencies. Furthermore, a data augmentation method named Cutin is proposed to boost the model's generalization ability and training performance. Experiments conducted on the PASCAL VOC2012 dataset demonstrate the effectiveness of our approach. Specifically, at annotation ratios of 1/16, 1/8, and 1/4, our model achieves improvements of 0.53%, 0.87%, and 0.85% in mIoU accuracy, respectively, compared to the baseline model. These results validate the progressiveness of our model, which generates higher-quality pseudo-labels to enhance segmentation performance. The related codes and results have been released at https://github.com/GuYuIMUST/STI.