Micro-expression recognition (MER) has drawn increasing attention in recent
years due to its potential applications in intelligent medical and lie
detection. However, the shortage of annotated data has been the major obstacle
to further improve deep-learning based MER methods. Intuitively, utilizing
sufficient macro-expression data to promote MER performance seems to be a
feasible solution. However, the facial patterns of macro-expressions and
micro-expressions are significantly different, which makes naive transfer
learning methods difficult to deploy directly. To tacle this issue, we propose
a generalized transfer learning paradigm, called \textbf{MA}cro-expression
\textbf{TO} \textbf{MI}cro-expression (MA2MI). Under our paradigm, networks can
learns the ability to represent subtle facial movement by reconstructing future
frames. In addition, we also propose a two-branch micro-action network
(MIACNet) to decouple facial position features and facial action features,
which can help the network more accurately locate facial action locations.
Extensive experiments on three popular MER benchmarks demonstrate the
superiority of our method.

微表情识别是近年来备受关注的研究领域，在智能医学和骗术检测等方面具有潜在应用。然而，缺乏标注数据一直以来都是加强基于深度学习的微表情识别方法的主要障碍之一。本文提出了一种广义的转移学习范式，称为宏观表情到微表情（MA2MI）。在该范式下，网络可以通过重建未来帧来学习表示微妙的面部运动。此外，本文还提出了一种两支路微动作网络（MIACNet），用于解耦面部位置特征和面部动作特征，帮助网络更准确地定位面部动作位置。在三个常用的微表情识别基准上进行的大量实验证明了我们方法的优越性。

宏观到微观：通过在宏观表情视频上进行预训练来提升微表情识别

From Macro to Micro: Boosting micro-expression recognition via  pre-training on macro-expression videos

Are current language models capable of deception and lie detection? We study
this question by introducing a text-based game called $\textit{Hoodwinked}$,
inspired by $\textit{Mafia}$ and $\textit{Among Us}$. Players are locked in a
house and must find a key to escape, but one player is tasked with killing the
others. Each time a murder is committed, the surviving players have a natural
language discussion then vote to banish one player from the game. We conduct
experiments with agents controlled by GPT-3, GPT-3.5, and GPT-4 and find
evidence of deception and lie detection capabilities. The killer often denies
their crime and accuses others, leading to measurable effects on voting
outcomes. More advanced models are more effective killers, outperforming
smaller models in 18 of 24 pairwise comparisons. Secondary metrics provide
evidence that this improvement is not mediated by different actions, but rather
by stronger deception capabilities during discussions. Overall, we find
substantial evidence that current language models are capable of deception. To
better evaluate the ability of AI agents to deceive humans, we make this game
publicly available at this https URL .

当前的语言模型是否具有欺骗和识别谎言的能力？我们通过引入一个名为 “Hoodwinked” 的基于文本的游戏，并与 GPT-3、GPT-3.5 和 GPT-4 控制的代理进行实验，发现这些模型具有欺骗和识别谎言的能力，并且较为先进的模型在击败其他模型方面更加有效。

蒙蔽：基于文本游戏中的欺骗与合作

Hoodwinked: Deception and Cooperation in a Text-Based Game for Language  Models

Unlike the conventional facial expressions, micro-expressions are involuntary
and transient facial expressions capable of revealing the genuine emotions that
people attempt to hide. Therefore, they can provide important information in a
broad range of applications such as lie detection, criminal detection, etc.
Since micro-expressions are transient and of low intensity, however, their
detection and recognition is difficult and relies heavily on expert
experiences. Due to its intrinsic particularity and complexity, video-based
micro-expression analysis is attractive but challenging, and has recently
become an active area of research. Although there have been numerous
developments in this area, thus far there has been no comprehensive survey that
provides researchers with a systematic overview of these developments with a
unified evaluation. Accordingly, in this survey paper, we first highlight the
key differences between macro- and micro-expressions, then use these
differences to guide our research survey of video-based micro-expression
analysis in a cascaded structure, encompassing the neuropsychological basis,
datasets, features, spotting algorithms, recognition algorithms, applications
and evaluation of state-of-the-art approaches. For each aspect, the basic
techniques, advanced developments and major challenges are addressed and
discussed. Furthermore, after considering the limitations of existing
micro-expression datasets, we present and release a new dataset - called
micro-and-macro expression warehouse (MMEW) - containing more video samples and
more labeled emotion types. We then perform a unified comparison of
representative methods on CAS(ME)2 for spotting, and on MMEW and SAMM for
recognition, respectively. Finally, some potential future research directions
are explored and outlined.

该研究论文系统概述了基于视频的微表情分析，讨论了其神经心理学基础、数据集、特征、算法、应用和评估，之后提出了一个更完整的、适用范围更广的数据集，并进行了方法比较和未来研究方向的展望。