Infrared and visible image fusion task aims to generate a fused image which
contains salient features and rich texture details from multi-source images.
However, under complex illumination conditions, few algorithms pay attention to
the edge information of local regions which is crucial for downstream tasks. To
this end, we propose a fusion network based on the local edge enhancement,
named LE2Fusion. Specifically, a local edge enhancement (LE2) module is
proposed to improve the edge information under complex illumination conditions
and preserve the essential features of image. For feature extraction, a
multi-scale residual attention (MRA) module is applied to extract rich
features. Then, with LE2, a set of enhancement weights are generated which are
utilized in feature fusion strategy and used to guide the image reconstruction.
To better preserve the local detail information and structure information, the
pixel intensity loss function based on the local region is also presented. The
experiments demonstrate that the proposed method exhibits better fusion
performance than the state-of-the-art fusion methods on public datasets.

本文提出了一种基于本地边缘增强的融合网络，该网络可有效增强图像的边缘信息，并具有较好的图像融合性能。

LE2Fusion：一种用于红外和可见光图像融合的新型局部边缘增强模块

LE2Fusion: A novel local edge enhancement module for infrared and  visible image fusion

3D scene flow estimation aims to estimate point-wise motions between two
consecutive frames of point clouds. Superpoints, i.e., points with similar
geometric features, are usually employed to capture similar motions of local
regions in 3D scenes for scene flow estimation. However, in existing methods,
superpoints are generated with the offline clustering methods, which cannot
characterize local regions with similar motions for complex 3D scenes well,
leading to inaccurate scene flow estimation. To this end, we propose an
iterative end-to-end superpoint based scene flow estimation framework, where
the superpoints can be dynamically updated to guide the point-level flow
prediction. Specifically, our framework consists of a flow guided superpoint
generation module and a superpoint guided flow refinement module. In our
superpoint generation module, we utilize the bidirectional flow information at
the previous iteration to obtain the matching points of points and superpoint
centers for soft point-to-superpoint association construction, in which the
superpoints are generated for pairwise point clouds. With the generated
superpoints, we first reconstruct the flow for each point by adaptively
aggregating the superpoint-level flow, and then encode the consistency between
the reconstructed flow of pairwise point clouds. Finally, we feed the
consistency encoding along with the reconstructed flow into GRU to refine
point-level flow. Extensive experiments on several different datasets show that
our method can achieve promising performance.

本文提出了一种基于可动态更新超级点的 3D 场景流估计框架，该框架结合超级点生成模块和超级点导向流细化模块，通过流提导超级点的生成和可适应聚合超级点级流来重建点的流，并将一致性编码和重建流输入 GRU 以细化点级流，实现了对复杂 3D 场景的准确估计。

由超点引导的无监督 3D 场景流估计

Self-Supervised 3D Scene Flow Estimation Guided by Superpoints

Previous works on vehicle Re-ID mainly focus on extracting global features
and learning distance metrics. Because some vehicles commonly share same model
and maker, it is hard to distinguish them based on their global appearances.
Compared with the global appearance, local regions such as decorations and
inspection stickers attached to the windshield, may be more distinctive for
vehicle Re-ID. To embed the detailed visual cues in those local regions, we
propose a Region-Aware deep Model (RAM). Specifically, in addition to
extracting global features, RAM also extracts features from a series of local
regions. As each local region conveys more distinctive visual cues, RAM
encourages the deep model to learn discriminative features. We also introduce a
novel learning algorithm to jointly use vehicle IDs, types/models, and colors
to train the RAM. This strategy fuses more cues for training and results in
more discriminative global and regional features. We evaluate our methods on
two large-scale vehicle Re-ID datasets, i.e., VeRi and VehicleID. Experimental
results show our methods achieve promising performance in comparison with
recent works.

提出了一种区域感知深度模型（RAM），不仅可以提取全局特征用于车辆 Re-ID，更可以从一系列局部区域中提取特征，利用车辆 ID、类型 / 型号和颜色，联合训练 RAM，融合更多信息用于训练，获得更具区分度的特征，并在 VeRi 和 VehicleID 两个大规模车辆 Re-ID 数据集上得到了良好的实验效果。