The growing availability of commodity RGB-D cameras has boosted the
applications in the field of scene understanding. However, as a fundamental
scene understanding task, surface normal estimation from RGB-D data lacks
thorough investigation. In this paper, a hierarchical fusion network with
adaptive feature re-weighting is proposed for surface normal estimation from a
single RGB-D image. Specifically, the features from color image and depth are
successively integrated at multiple scales to ensure global surface smoothness
while preserving visually salient details. Meanwhile, the depth features are
re-weighted with a confidence map estimated from depth before merging into the
color branch to avoid artifacts caused by input depth corruption. Additionally,
a hybrid multi-scale loss function is designed to learn accurate normal
estimation given noisy ground-truth dataset. Extensive experimental results
validate the effectiveness of the fusion strategy and the loss design,
outperforming state-of-the-art normal estimation schemes.

本论文提出了一种基于 RGB-D 图像的表面法向估计的分层融合网络和自适应特征再加权，设计了混合多尺度损失函数来学习准确的法向估计，实验证明了该方法在场景理解中的有效性。

基于分层 RGB-D 融合的深度表面法线估计

Deep Surface Normal Estimation with Hierarchical RGB-D Fusion

Inspired by classic generative adversarial networks (GAN), we propose a novel
end-to-end adversarial neural network, called SegAN, for the task of medical
image segmentation. Since image segmentation requires dense, pixel-level
labeling, the single scalar real/fake output of a classic GAN's discriminator
may be ineffective in producing stable and sufficient gradient feedback to the
networks. Instead, we use a fully convolutional neural network as the segmentor
to generate segmentation label maps, and propose a novel adversarial critic
network with a multi-scale $L_1$ loss function to force the critic and
segmentor to learn both global and local features that capture long- and
short-range spatial relationships between pixels. In our SegAN framework, the
segmentor and critic networks are trained in an alternating fashion in a
min-max game: The critic takes as input a pair of images, (original_image $*$
predicted_label_map, original_image $*$ ground_truth_label_map), and then is
trained by maximizing a multi-scale loss function; The segmentor is trained
with only gradients passed along by the critic, with the aim to minimize the
multi-scale loss function. We show that such a SegAN framework is more
effective and stable for the segmentation task, and it leads to better
performance than the state-of-the-art U-net segmentation method. We tested our
SegAN method using datasets from the MICCAI BRATS brain tumor segmentation
challenge. Extensive experimental results demonstrate the effectiveness of the
proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance
comparable to the state-of-the-art for whole tumor and tumor core segmentation
while achieves better precision and sensitivity for Gd-enhance tumor core
segmentation; on BRATS 2015 SegAN achieves better performance than the
state-of-the-art in both dice score and precision.

本研究提出了一种名为 SegAN 的端到端对抗神经网络，用于医学图像分割任务，它通过采用全卷积神经网络作为段分割器、提出一种新的对抗批判者网络，并使用多尺度 L1 损失函数来强制批判者和分割器学习全局和局部特征，从而实现更稳定、更有效的分割。在使用来自 MICCAI BRATS 脑肿瘤分割挑战的数据集进行测试后，表明 SegAN 具有优异的性能。

SegAN: 医学图像分割的多尺度 L1 损失对抗网络

SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image  Segmentation

Lossy image compression algorithms are pervasively used to reduce the size of
images transmitted over the web and recorded on data storage media. However, we
pay for their high compression rate with visual artifacts degrading the user
experience. Deep convolutional neural networks have become a widespread tool to
address high-level computer vision tasks very successfully. Recently, they have
found their way into the areas of low-level computer vision and image
processing to solve regression problems mostly with relatively shallow
networks.
We present a novel 12-layer deep convolutional network for image compression
artifact suppression with hierarchical skip connections and a multi-scale loss
function. We achieve a boost of up to 1.79 dB in PSNR over ordinary JPEG and an
improvement of up to 0.36 dB over the best previous ConvNet result. We show
that a network trained for a specific quality factor (QF) is resilient to the
QF used to compress the input image - a single network trained for QF 60
provides a PSNR gain of more than 1.5 dB over the wide QF range from 40 to 76.

本研究提出了一种新型的 12 层深度卷积神经网络，用于图像压缩伪影抑制，其具有分层跳过连接和多尺度损失函数。相较于普通的 JPEG，可提高高达 1.79 dB 的峰值信噪比，并且相较于先前最佳 ConvNet 结果提高高达 0.36 dB。该研究表明，训练用于特定质量因子 (QF) 的网络对于压缩输入图像所使用的 QF 是有韧性的，单个针对 QF 60 训练的网络在多种 QF 范围内 (40 至 76) 提供超过 1.5 dB 的 PSNR 增益。