A true interpreting agent not only understands sign language and translates
to text, but also understands text and translates to signs. Much of the AI work
in sign language translation to date has focused mainly on translating from
signs to text. Towards the latter goal, we propose a text-to-sign translation
model, SignNet, which exploits the notion of similarity (and dissimilarity) of
visual signs in translating. This module presented is only one part of a
dual-learning two task process involving text-to-sign (T2S) as well as
sign-to-text (S2T). We currently implement SignNet as a single channel
architecture so that the output of the T2S task can be fed into S2T in a
continuous dual learning framework. By single channel, we refer to a single
modality, the body pose joints.
In this work, we present SignNet, a T2S task using a novel metric embedding
learning process, to preserve the distances between sign embeddings relative to
their dissimilarity. We also describe how to choose positive and negative
examples of signs for similarity testing. From our analysis, we observe that
metric embedding learning-based model perform significantly better than the
other models with traditional losses, when evaluated using BLEU scores. In the
task of gloss to pose, SignNet performed as well as its state-of-the-art (SoTA)
counterparts and outperformed them in the task of text to pose, by showing
noteworthy enhancements in BLEU 1 - BLEU 4 scores (BLEU 1: 31->39; ~26%
improvement and BLEU 4: 10.43->11.84; ~14\% improvement) when tested on the
popular RWTH PHOENIX-Weather-2014T benchmark dataset

本文介绍了一种基于度量嵌入学习的方法，利用相似性（和不相似性）来进行文本到手语的翻译，并说明了如何选择手语的正负例进行相似性测试。在 RWTH PHOENIX-Weather-2014T 基准数据集上的测试中，使用 SignNet 模型取得了比现有技术更好的表现。

SignNet：利用度量嵌入学习生成单通道符号

SignNet: Single Channel Sign Generation using Metric Embedded Learning

Membership inference (MI) determines if a sample was part of a victim model
training set. Recent development of MI attacks focus on record-level membership
inference which limits their application in many real-world scenarios. For
example, in the person re-identification task, the attacker (or investigator)
is interested in determining if a user's images have been used during training
or not. However, the exact training images might not be accessible to the
attacker. In this paper, we develop a user-level MI attack where the goal is to
find if any sample from the target user has been used during training even when
no exact training sample is available to the attacker. We focus on metric
embedding learning due to its dominance in person re-identification, where
user-level MI attack is more sensible. We conduct an extensive evaluation on
several datasets and show that our approach achieves high accuracy on
user-level MI task.

本文提出了基于度量嵌入学习的用户级成员推理攻击，旨在确定目标用户是否曾用于训练，即使攻击者无法获得确切的训练样本。在几个数据集上进行广泛评估，结果表明我们的方法在用户级成员推理任务中取得了高准确度。