Deep image hashing aims to map input images into simple binary hash codes via
deep neural networks and thus enable effective large-scale image retrieval.
Recently, hybrid networks that combine convolution and Transformer have
achieved superior performance on various computer tasks and have attracted
extensive attention from researchers. Nevertheless, the potential benefits of
such hybrid networks in image retrieval still need to be verified. To this end,
we propose a hybrid convolutional and self-attention deep hashing method known
as HybridHash. Specifically, we propose a backbone network with stage-wise
architecture in which the block aggregation function is introduced to achieve
the effect of local self-attention and reduce the computational complexity. The
interaction module has been elaborately designed to promote the communication
of information between image blocks and to enhance the visual representations.
We have conducted comprehensive experiments on three widely used datasets:
CIFAR-10, NUS-WIDE and IMAGENET. The experimental results demonstrate that the
method proposed in this paper has superior performance with respect to
state-of-the-art deep hashing methods. Source code is available
this https URL

通过深度神经网络将输入图像映射为简单的二进制哈希码，从而实现有效的大规模图像检索。我们提出了一种名为 HybridHash 的混合卷积和自注意力深度哈希方法，通过引入分阶段架构的骨干网络和块聚合函数来实现局部自注意力效果，并降低计算复杂性，以提高图像块之间的信息交流和视觉表示。实验结果表明，本文提出的方法在三个广泛使用的数据集上具有优越的性能，超过了现有的深度哈希方法。

混合哈希：用于图像检索的混合卷积和自注意力深度哈希

HybridHash: Hybrid Convolutional and Self-Attention Deep Hashing for  Image Retrieval

Current fifth generation (5G) cellular networks mainly focus on the
terrestrial scenario. Due to the difficulty of deploying communications
infrastructure on the ocean, the performance of existing maritime communication
networks (MCNs) is far behind 5G. This problem can be solved by using unmanned
aerial vehicles (UAVs) as agile aerial platforms to enable on-demand maritime
coverage, as a supplement to marine satellites and shore-based terrestrial
based stations (TBSs). In this paper, we study the integration of UAVs with
existing MCNs, and investigate the potential gains of hybrid
satellite-UAV-terrestrial networks for maritime coverage. Unlike the
terrestrial scenario, vessels on the ocean keep to sea lanes and are sparsely
distributed. This provides new opportunities to ease the scheduling of UAVs.
Also, new challenges arise due to the more complicated maritime prorogation
environment, as well as the mutual interference between UAVs and existing
satellites/TBSs. We discuss these issues and show possible solutions
considering practical constraints.

本文探讨了使用无人机作为航空平台以提高海上通信网络覆盖率的可行性以及如何在 考虑到现实约束的情况下整合现有海上通信网络，研究了混合的卫星 - 无人机 - 陆地网络对于提高海 上覆盖率的潜在收益。

实现海洋上的 5G：一种混合卫星 - 无人机 - 地面网络解决方案

Enabling 5G on the Ocean: A Hybrid Satellite-UAV-Terrestrial Network  Solution

This paper investigates body bones from skeleton data for skeleton based
action recognition. Body joints, as the direct result of mature pose estimation
technologies, are always the key concerns of traditional action recognition
methods. However, instead of joints, we humans naturally identify how the human
body moves according to shapes, lengths and places of bones, which are more
obvious and stable for observation. Hence given graphs generated from skeleton
data, we propose to develop convolutions over graph edges that correspond to
bones in human skeleton. We describe an edge by integrating its spatial
neighboring edges to explore the cooperation between different bones, as well
as its temporal neighboring edges to address the consistency of movements in an
action. A graph edge convolutional neural network is then designed for skeleton
based action recognition. Considering the complementarity between graph node
convolution and graph edge convolution, we additionally construct two hybrid
neural networks to combine graph node convolutional neural network and graph
edge convolutional neural network using shared intermediate layers.
Experimental results on Kinetics and NTU-RGB+D datasets demonstrate that our
graph edge convolution is effective to capture characteristic of actions and
our graph edge convolutional neural network significantly outperforms existing
state-of-art skeleton based action recognition methods. Additionally, more
performance improvements can be achieved by the hybrid networks.

本文研究通过骨架数据识别人体骨骼运动的方法，提出了基于图边卷积神经网络的方法，通过对骨骼边缘的空间和时间邻居关系的探究来识别人体运动，实验结果表明该方法可以更有效地识别人体运动，且融合节点卷积和图边卷积的混合模型可以进一步提高性能。