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.

通过将分类器网络与逆向 HRNet 架构集成，本文介绍了一种新颖的分割框架，用于高效的图像分割。我们的方法利用半监督预训练的 ResNet-50 骨干网络，在不同尺度上生成特征图，然后通过适应具有不同通道维度的逆向 HRNet 进行处理，产生最终的分割结果。在多个基准数据集上进行了严格测试，包括 Mapillary Vistas、Cityscapes、CamVid、COCO 和 PASCAL-VOC2012，使用像素精度和平均交并比（mIoU）等指标评估了分割性能。结果表明，我们提出的模型在实现高分割精度方面具有较高的效果，表明其在图像分析等各种应用中具有潜力。通过整合 ResNet-50 和逆向 HRNet 的优势，我们提出了对图像分割挑战的强大解决方案。