In this paper, we investigate the impacts of three main aspects of visual
tracking, i.e., the backbone network, the attentional mechanism, and the
detection component, and propose a Siamese Attentional Keypoint Network, dubbed
SATIN, for efficient tracking and accurate localization. Firstly, a new Siamese
lightweight hourglass network is specially designed for visual tracking. It
takes advantage of the benefits of the repeated bottom-up and top-down
inference to capture more global and local contextual information at multiple
scales. Secondly, a novel cross-attentional module is utilized to leverage both
channel-wise and spatial intermediate attentional information, which can
enhance both discriminative and localization capabilities of feature maps.
Thirdly, a keypoints detection approach is invented to trace any target object
by detecting the top-left corner point, the centroid point, and the
bottom-right corner point of its bounding box. Therefore, our SATIN tracker not
only has a strong capability to learn more effective object representations,
but also is computational and memory storage efficiency, either during the
training or testing stages. To the best of our knowledge, we are the first to
propose this approach. Without bells and whistles, experimental results
demonstrate that our approach achieves state-of-the-art performance on several
recent benchmark datasets, at a speed far exceeding 27 frames per second.

本研究提出了一种名为 SATIN 的 Siamese Attentioal Keypoint Network 用于高效追踪和准确定位目标，其可利用轻量级沙漏网络、交叉注意力模块和关键点检测方法来提高特征图的辨别力和定位能力，并在多个基准数据集上获得了最新的性能表现。

基于孪生关注关键点网络的高性能视觉跟踪

Siamese Attentional Keypoint Network for High Performance Visual  Tracking

Introducing attentional mechanism in neural network is a powerful concept,
and has achieved impressive results in many natural language processing tasks.
However, most of the existing models impose attentional distribution on a flat
topology, namely the entire input representation sequence. Clearly, any
well-formed sentence has its accompanying syntactic tree structure, which is a
much rich topology. Applying attention to such topology not only exploits the
underlying syntax, but also makes attention more interpretable. In this paper,
we explore this direction in the context of natural language inference. The
results demonstrate its efficacy. We also perform extensive qualitative
analysis, deriving insights and intuitions of why and how our model works.

本文探讨了在自然语言推理的背景下，将注意力机制应用到句法树这种更加丰富的拓扑结构上，不仅可以利用底层的句法信息，还可以使注意力更具可解释性。结果表明，该方法非常有效，并进行了广泛的质量分析，提取出了为什么以及如何工作的见解和直觉。

基于语法的自然语言推理注意力模型

Syntax-based Attention Model for Natural Language Inference

The attentional mechanism has proven to be effective in improving end-to-end
neural machine translation. However, due to the intricate structural divergence
between natural languages, unidirectional attention-based models might only
capture partial aspects of attentional regularities. We propose agreement-based
joint training for bidirectional attention-based end-to-end neural machine
translation. Instead of training source-to-target and target-to-source
translation models independently,our approach encourages the two complementary
models to agree on word alignment matrices on the same training data.
Experiments on Chinese-English and English-French translation tasks show that
agreement-based joint training significantly improves both alignment and
translation quality over independent training.

通过协商双向翻译模型之间的单词对齐矩阵来优化神经机器翻译的关注机制，并提高翻译质量。

基于协议的双向注意力神经机器翻译联合训练

Agreement-based Joint Training for Bidirectional Attention-based Neural  Machine Translation

An attentional mechanism has lately been used to improve neural machine
translation (NMT) by selectively focusing on parts of the source sentence
during translation. However, there has been little work exploring useful
architectures for attention-based NMT. This paper examines two simple and
effective classes of attentional mechanism: a global approach which always
attends to all source words and a local one that only looks at a subset of
source words at a time. We demonstrate the effectiveness of both approaches
over the WMT translation tasks between English and German in both directions.
With local attention, we achieve a significant gain of 5.0 BLEU points over
non-attentional systems which already incorporate known techniques such as
dropout. Our ensemble model using different attention architectures has
established a new state-of-the-art result in the WMT'15 English to German
translation task with 25.9 BLEU points, an improvement of 1.0 BLEU points over
the existing best system backed by NMT and an n-gram reranker.

本文研究了两种简单而有效的注意力机制 —— 全局式和局部式，证明了这两种方法在英德翻译任务中的有效性，并使用不同的注意力架构建立了一个新的 WMT'15 英德翻译任务最佳结果的集成模型，其 BLEU 分数为 25.9 分，比现有的最佳基于 NMT 和 n-gram reranker 的系统提高 1.0 分。