Online segmentation of laser-induced damage on large-aperture optics in
high-power laser facilities is challenged by complicated damage morphology,
uneven illumination and stray light interference. Fully supervised semantic
segmentation algorithms have achieved state-of-the-art performance, but rely on
plenty of pixel-level labels, which are time-consuming and labor-consuming to
produce. LayerCAM, an advanced weakly supervised semantic segmentation
algorithm, can generate pixel-accurate results using only image-level labels,
but its scattered and partially under-activated class activation regions
degrade segmentation performance. In this paper, we propose a weakly supervised
semantic segmentation method with Continuous Gradient CAM and its nonlinear
multi-scale fusion (CG-fusion CAM). The method redesigns the way of
back-propagating gradients and non-linearly activates the multi-scale fused
heatmaps to generate more fine-grained class activation maps with appropriate
activation degree for different sizes of damage sites. Experiments on our
dataset show that the proposed method can achieve segmentation performance
comparable to that of fully supervised algorithms.

本文提出了一种基于连续梯度 CAM 及其非线性多尺度融合 (CG-fusion CAM) 的弱监督语义分割方法，通过重新设计梯度的反向传播方式和非线性激活多尺度融合热图，生成了更精细的类激活图，适合不同大小的损伤区域，实验结果表明所提出的方法的分割性能可与全监督算法相媲美。

CG-fusion CAM: 大孔径光学元件激光损伤在线分割

CG-fusion CAM: Online segmentation of laser-induced damage on  large-aperture optics

Semantic segmentation has recently witnessed major progress, where fully
convolutional neural networks have shown to perform well. However, most of the
previous work focused on improving single image segmentation. To our knowledge,
no prior work has made use of temporal video information in a recurrent
network. In this paper, we introduce a novel approach to implicitly utilize
temporal data in videos for online semantic segmentation. The method relies on
a fully convolutional network that is embedded into a gated recurrent
architecture. This design receives a sequence of consecutive video frames and
outputs the segmentation of the last frame. Convolutional gated recurrent
networks are used for the recurrent part to preserve spatial connectivities in
the image. Our proposed method can be applied in both online and batch
segmentation. This architecture is tested for both binary and semantic video
segmentation tasks. Experiments are conducted on the recent benchmarks in
SegTrack V2, Davis, CityScapes, and Synthia. Using recurrent fully
convolutional networks improved the baseline network performance in all of our
experiments. Namely, 5% and 3% improvement of F-measure in SegTrack2 and Davis
respectively, 5.7% improvement in mean IoU in Synthia and 3.5% improvement in
categorical mean IoU in CityScapes. The performance of the RFCN network depends
on its baseline fully convolutional network. Thus RFCN architecture can be seen
as a method to improve its baseline segmentation network by exploiting
spatiotemporal information in videos.

本文提出了一种基于全卷积神经网络和门控循环体系结构的循环全卷积网络，利用视频中的时间信息进行在线语义分割， 大幅提高了分割的准确性，可以在二进制和语义视频分割任务中应用。