This paper presents a review of techniques used for the detection and
tracking of UAVs or drones. There are different techniques that depend on
collecting measurements of the position, velocity, and image of the UAV and
then using them in detection and tracking. Hybrid detection techniques are also
presented. The paper is a quick reference for a wide spectrum of methods that
are used in the drone detection process.

本论文介绍了用于检测和跟踪无人机或飞行器的技术，这些技术依赖于收集无人机的位置、速度和图像测量，然后使用它们进行检测和跟踪，同时介绍了混合检测技术。该论文是无人机检测过程中使用的各种方法的快速参考。

无人机视觉检测和跟踪方法综述

A review of UAV Visual Detection and Tracking Methods

Fine-grained information on translation errors is helpful for the translation
evaluation community. Existing approaches can not synchronously consider error
position and type, failing to integrate the error information of both. In this
paper, we propose Fine-Grained Translation Error Detection (FG-TED) task,
aiming at identifying both the position and the type of translation errors on
given source-hypothesis sentence pairs. Besides, we build an FG-TED model to
predict the \textbf{addition} and \textbf{omission} errors -- two typical
translation accuracy errors. First, we use a word-level classification paradigm
to form our model and use the shortcut learning reduction to relieve the
influence of monolingual features. Besides, we construct synthetic datasets for
model training, and relieve the disagreement of data labeling in authoritative
datasets, making the experimental benchmark concordant. Experiments show that
our model can identify both error type and position concurrently, and gives
state-of-the-art results on the restored dataset. Our model also delivers more
reliable predictions on low-resource and transfer scenarios than existing
baselines. The related datasets and the source code will be released in the
future.

本文提出 Fine-Grained Translation Error Detection（FG-TED）任务，并建立了模型，旨在同时识别给定源 - 假设句对中的翻译错误位置和类型。实验结果表明，我们的模型可以同时识别错误类型和位置，并在恢复数据集上具有最先进的效果。

朝向细粒度信息：识别翻译错误的类型和位置

Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors

In this paper, we present a so-called interlaced sparse self-attention
approach to improve the efficiency of the \emph{self-attention} mechanism for
semantic segmentation. The main idea is that we factorize the dense affinity
matrix as the product of two sparse affinity matrices. There are two successive
attention modules each estimating a sparse affinity matrix. The first attention
module is used to estimate the affinities within a subset of positions that
have long spatial interval distances and the second attention module is used to
estimate the affinities within a subset of positions that have short spatial
interval distances. These two attention modules are designed so that each
position is able to receive the information from all the other positions. In
contrast to the original self-attention module, our approach decreases the
computation and memory complexity substantially especially when processing
high-resolution feature maps. We empirically verify the effectiveness of our
approach on six challenging semantic segmentation benchmarks.

本文提出一种称为交错稀疏自注意力的方法，通过将密集的亲和力矩阵因子分解为两个稀疏的亲和力矩阵，来提高自我注意机制在语义分割中的效率。