This paper introduces a novel segmentation framework that integrates a classifier network with a reverse HRNet architecture for efficient image segmentation. Our approach utilizes a ResNet-50 backbone, pretrained in a semi-supervised manner, to generate feature maps at various scales. These maps are then processed by a reverse HRNet, which is adapted to handle varying channel dimensions through 1x1 convolutions, to produce the final segmentation output. We strategically avoid fine-tuning the backbone network to minimize memory consumption during training. Our methodology is rigorously tested across several benchmark datasets including Mapillary Vistas, Cityscapes, CamVid, COCO, and PASCAL-VOC2012, employing metrics such as pixel accuracy and mean Intersection over Union (mIoU) to evaluate segmentation performance. The results demonstrate the effectiveness of our proposed model in achieving high segmentation accuracy, indicating its potential for various applications in image analysis. By leveraging the strengths of both the ResNet-50 and reverse HRNet within a unified framework, we present a robust solution to the challenges of image segmentation.

翻译：本文提出了一种新颖的分割框架，该框架将分类器网络与反向HRNet架构相结合，以实现高效的图像分割。我们的方法采用半监督预训练的ResNet-50主干网络生成多尺度特征图，随后由反向HRNet（通过1×1卷积适应不同通道维度）处理这些特征图以生成最终分割输出。我们战略性地避免微调主干网络，以降低训练过程中的内存消耗。该方法在Mapillary Vistas、Cityscapes、CamVid、COCO和PASCAL-VOC2012等多个基准数据集上进行了严格测试，采用像素精度和平均交并比（mIoU）等指标评估分割性能。结果表明，所提模型在实现高分割精度方面具有显著效果，展现了其在图像分析领域的应用潜力。通过将ResNet-50与反向HRNet的优势统一于一个框架中，我们为图像分割的挑战提供了鲁棒的解决方案。