Image inpainting has achieved fundamental advances with deep learning.
However, almost all existing inpainting methods aim to process natural images,
while few target Thermal Infrared (TIR) images, which have widespread
applications. When applied to TIR images, conventional inpainting methods
usually generate distorted or blurry content. In this paper, we propose a novel
task -- Thermal Infrared Image Inpainting, which aims to reconstruct missing
regions of TIR images. Crucially, we propose a novel deep-learning-based model
TIR-Fill. We adopt the edge generator to complete the canny edges of broken TIR
images. The completed edges are projected to the normalization weights and
biases to enhance edge awareness of the model. In addition, a refinement
network based on gated convolution is employed to improve TIR image
consistency. The experiments demonstrate that our method outperforms
state-of-the-art image inpainting approaches on FLIR thermal dataset.

本文提出了一种新的任务 —— 热红外图像修复，引入深度学习模型 TIR-Fill 和采用基于门卷积的细化网络，旨在解决现有 inpainting 方法在处理 TIR 图像时普遍存在的失真和模糊问题，并在 FLIR thermal 数据集上表现出了优异的性能。

基于边缘感知引导的热红外图像修复

Thermal Infrared Image Inpainting via Edge-Aware Guidance

Spatial-temporal data forecasting of traffic flow is a challenging task
because of complicated spatial dependencies and dynamical trends of temporal
pattern between different roads. Existing frameworks typically utilize given
spatial adjacency graph and sophisticated mechanisms for modeling spatial and
temporal correlations. However, limited representations of given spatial graph
structure with incomplete adjacent connections may restrict effective
spatial-temporal dependencies learning of those models. To overcome those
limitations, our paper proposes Spatial-Temporal Fusion Graph Neural Networks
(STFGNN) for traffic flow forecasting. SFTGNN could effectively learn hidden
spatial-temporal dependencies by a novel fusion operation of various spatial
and temporal graphs, which is generated by a data-driven method. Meanwhile, by
integrating this fusion graph module and a novel gated convolution module into
a unified layer, SFTGNN could handle long sequences. Experimental results on
several public traffic datasets demonstrate that our method achieves
state-of-the-art performance consistently than other baselines.

本文提出一种名为 STFGNN 的基于图神经网络的交通流预测模型，结合空间和时间各种图的融合操作，以及门控卷积模块来处理长序列，实现了比其它基线更好的性能。

基于时空融合的图神经网络用于交通流量预测

Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow  Forecasting

Human pose transfer, as a misaligned image generation task, is very
challenging. Existing methods cannot effectively utilize the input information,
which often fail to preserve the style and shape of hair and clothes. In this
paper, we propose an adaptive human pose transfer network with two hierarchical
deformation levels. The first level generates human semantic parsing aligned
with the target pose, and the second level generates the final textured person
image in the target pose with the semantic guidance. To avoid the drawback of
vanilla convolution that treats all the pixels as valid information, we use
gated convolution in both two levels to dynamically select the important
features and adaptively deform the image layer by layer. Our model has very few
parameters and is fast to converge. Experimental results demonstrate that our
model achieves better performance with more consistent hair, face and clothes
with fewer parameters than state-of-the-art methods. Furthermore, our method
can be applied to clothing texture transfer.

本文提出了一个基于两级分层变形的自适应人体姿态转移网络，使用门控卷积动态选择图像的重要特征并逐层自适应变形，通过更少的参数快速收敛并在服装纹理转移等任务中获得更好的性能。