The state-of-the-art neural video codecs have outperformed the most
sophisticated traditional codecs in terms of RD performance in certain cases.
However, utilizing them for practical applications is still challenging for two
major reasons. 1) Cross-platform computational errors resulting from floating
point operations can lead to inaccurate decoding of the bitstream. 2) The high
computational complexity of the encoding and decoding process poses a challenge
in achieving real-time performance. In this paper, we propose a real-time
cross-platform neural video codec, which is capable of efficiently decoding of
720P video bitstream from other encoding platforms on a consumer-grade GPU.
First, to solve the problem of inconsistency of codec caused by the uncertainty
of floating point calculations across platforms, we design a calibration
transmitting system to guarantee the consistent quantization of entropy
parameters between the encoding and decoding stages. The parameters that may
have transboundary quantization between encoding and decoding are identified in
the encoding stage, and their coordinates will be delivered by auxiliary
transmitted bitstream. By doing so, these inconsistent parameters can be
processed properly in the decoding stage. Furthermore, to reduce the bitrate of
the auxiliary bitstream, we rectify the distribution of entropy parameters
using a piecewise Gaussian constraint. Second, to match the computational
limitations on the decoding side for real-time video codec, we design a
lightweight model. A series of efficiency techniques enable our model to
achieve 25 FPS decoding speed on NVIDIA RTX 2080 GPU. Experimental results
demonstrate that our model can achieve real-time decoding of 720P videos while
encoding on another platform. Furthermore, the real-time model brings up to a
maximum of 24.2\% BD-rate improvement from the perspective of PSNR with the
anchor H.265.

我们提出了一个实时跨平台神经视频编解码器，能够在消费级 GPU 上高效解码来自其他编码平台的 720P 视频比特流。

跨平台应用的实时神经视频编解码器的研究基于校准信息

Towards Real-Time Neural Video Codec for Cross-Platform Application  Using Calibration Information

In this work, we construct a large-scale dataset for Ground-to-Aerial Person
Search, named G2APS, which contains 31,770 images of 260,559 annotated bounding
boxes for 2,644 identities appearing in both of the UAVs and ground
surveillance cameras. To our knowledge, this is the first dataset for
cross-platform intelligent surveillance applications, where the UAVs could work
as a powerful complement for the ground surveillance cameras. To more
realistically simulate the actual cross-platform Ground-to-Aerial surveillance
scenarios, the surveillance cameras are fixed about 2 meters above the ground,
while the UAVs capture videos of persons at different location, with a variety
of view-angles, flight attitudes and flight modes. Therefore, the dataset has
the following unique characteristics: 1) drastic view-angle changes between
query and gallery person images from cross-platform cameras; 2) diverse
resolutions, poses and views of the person images under 9 rich real-world
scenarios. On basis of the G2APS benchmark dataset, we demonstrate detailed
analysis about current two-step and end-to-end person search methods, and
further propose a simple yet effective knowledge distillation scheme on the
head of the ReID network, which achieves state-of-the-art performances on both
of the G2APS and the previous two public person search datasets, i.e., PRW and
CUHK-SYSU. The dataset and source code available on
https://github.com/yqc123456/HKD_for_person_search.

构建了一个大规模的 Ground-to-Aerial Person Search 数据集 (G2APS)，其中包含两个 UAV 和地面监控摄像头的 31,770 张图像，260,559 个已标注边界框和 2,644 个身份的出现。通过对 G2APS 基准数据集进行详细分析，提出了一种简单而有效的知识蒸馏方案，该方案在 ReID 网络的头部取得了 G2APS 和之前的两个公共人物搜索数据集 (PRW 和 CUHK-SYSU) 的最新性能。

地面到空中的人物搜索：基准数据集和方法

Ground-to-Aerial Person Search: Benchmark Dataset and Approach

Social recommendation has shown promising improvements over traditional
systems since it leverages social correlation data as an additional input. Most
existing work assumes that all data are available to the recommendation
platform. However, in practice, user-item interaction data (e.g.,rating) and
user-user social data are usually generated by different platforms, and both of
which contain sensitive information. Therefore, "How to perform secure and
efficient social recommendation across different platforms, where the data are
highly-sparse in nature" remains an important challenge. In this work, we bring
secure computation techniques into social recommendation, and propose S3Rec, a
sparsity-aware secure cross-platform social recommendation framework. As a
result, our model can not only improve the recommendation performance of the
rating platform by incorporating the sparse social data on the social platform,
but also protect data privacy of both platforms. Moreover, to further improve
model training efficiency, we propose two secure sparse matrix multiplication
protocols based on homomorphic encryption and private information retrieval.
Our experiments on two benchmark datasets demonstrate the effectiveness of
S3Rec.

本研究提出了一种安全的、跨平台的社交推荐框架 S3Rec，在保护数据隐私的同时，能够提高推荐性能和模型训练效率。该框架利用安全计算技术，通过稀疏矩阵乘法实现平台间数据交互。实验结果表明 S3Rec 的有效性。

安全的跨平台社交推荐中利用数据稀疏性

Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation

Fully autonomous mobile robots have a multitude of potential applications,
but guaranteeing robust navigation performance remains an open research
problem. For many tasks such as repeated infrastructure inspection, item
delivery, or inventory transport, a route repeating capability can be
sufficient and offers potential practical advantages over a full navigation
stack. Previous teach and repeat research has achieved high performance in
difficult conditions predominantly by using sophisticated, expensive sensors,
and has often had high computational requirements. Biological systems, such as
small animals and insects like seeing ants, offer a proof of concept that
robust and generalisable navigation can be achieved with extremely limited
visual systems and computing power. In this work we create a novel asynchronous
formulation for teach and repeat navigation that fully utilises odometry
information, paired with a correction signal driven by much more
computationally lightweight visual processing than is typically required. This
correction signal is also decoupled from the robot's motor control, allowing
its rate to be modulated by the available computing capacity. We evaluate this
approach with extensive experimentation on two different robotic platforms, the
Consequential Robotics Miro and the Clearpath Jackal robots, across navigation
trials totalling more than 6000 metres in a range of challenging indoor and
outdoor environments. Our approach continues to succeed when multiple
state-of-the-art systems fail due to low resolution images, unreliable
odometry, or lighting change, while requiring significantly less compute. We
also - for the first time - demonstrate versatile cross-platform teach and
repeat without changing parameters, in which we learn to navigate a route with
one robot and repeat that route using a completely different robot.

本文研究了移动机器人的自主导航问题，通过异步教学和重复的策略，结合视觉处理和硬件测距的信息，实现了对室内和室外复杂环境的有效导航和运动控制，并且在不同机器人平台之间具有较好的通用性和重复性。