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。

语义分割的特征早期融合

Early Fusion of Features for Semantic Segmentation

Comprehensive surgical planning require complex patient-specific anatomical
models. For instance, functional muskuloskeletal simulations necessitate all
relevant structures to be segmented, which could be performed in real-time
using deep neural networks given sufficient annotated samples. Such large
datasets of multiple structure annotations are costly to procure and are often
unavailable in practice. Nevertheless, annotations from different studies and
centers can be readily available, or become available in the future in an
incremental fashion. We propose a class-incremental segmentation framework for
extending a deep network trained for some anatomical structure to yet another
structure using a small incremental annotation set. Through distilling
knowledge from the current state of the framework, we bypass the need for a
full retraining. This is a meta-method to extend any choice of desired deep
segmentation network with only a minor addition per structure, which makes it
suitable for lifelong class-incremental learning and applicable also for future
deep neural network architectures. We evaluated our methods on a public knee
dataset of 100 MR volumes. Through varying amount of incremental annotation
ratios, we show how our proposed method can retain the previous anatomical
structure segmentation performance superior to the conventional finetuning
approach. In addition, our framework inherently exploits transferable knowledge
from previously trained structures to incremental tasks, demonstrated by
superior results compared to non-incremental training. With the presented
method, new anatomical structures can be learned without catastrophic
forgetting of older structures and without extensive increase of memory and
complexity.

该论文提出了一种基于类增量的分割框架，通过从当前框架的状态中提炼出的知识，将任何所需的深度分割网络扩展到另一种结构，适用于终身类增量学习。该方法可以在不忘记旧结构的情况下学习新的解剖结构，并且不会严重增加内存和复杂性。

使用额外解剖结构扩展预训练分割网络

Extending Pretrained Segmentation Networks with Additional Anatomical  Structures

A critical challenge problem of scene change detection is that noisy changes
generated by varying illumination, shadows and camera viewpoint make variances
of a scene difficult to define and measure since the noisy changes and semantic
ones are entangled. Following the intuitive idea of detecting changes by
directly comparing dissimilarities between a pair of features, we propose a
novel fully Convolutional siamese metric Network(CosimNet) to measure changes
by customizing implicit metrics. To learn more discriminative metrics, we
utilize contrastive loss to reduce the distance between the unchanged feature
pairs and to enlarge the distance between the changed feature pairs.
Specifically, to address the issue of large viewpoint differences, we propose
Thresholded Contrastive Loss (TCL) with a more tolerant strategy to punish
noisy changes. We demonstrate the effectiveness of the proposed approach with
experiments on three challenging datasets: CDnet, PCD2015, and VL-CMU-CD. Our
approach is robust to lots of challenging conditions, such as illumination
changes, large viewpoint difference caused by camera motion and zooming. In
addition, we incorporate the distance metric into the segmentation framework
and validate the effectiveness through visualization of change maps and feature
distribution. The source code is available at
this https URL

该论文提出了一种用于场景变化检测的新型完全卷积孪生度量网络 (CosimNet)，采用对比损失来获得更具判别力度量，其中提出了基于阈值的对比损失 (TCL) 来处理由大视点差异引起的多噪音变化。最后将距离度量集成到分割框架中，并通过变化图和特征分布可视化证明了该方法的有效性。