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二千五百一十四章

新书启航，《海贼世界做阎王》开局成为路飞三哥，三兄弟顶上救出艾斯。从此传奇四兄弟的名声响彻四海，不多时炎帝艾斯的名声传出，新晋四皇震动大海。紧接着龙王萨博，战神龙魂，明王路飞的称号渐渐传出，从此新世界是传奇四兄弟的世界。时代是传奇四兄弟的时代。随着四兄弟君临海上，整个大海为之颤抖！

香非在萼

治愈

她很想他

【都市日常+甜腻恋爱+追夫+病娇】（三位强势姐姐）那天，沈妄念冒着大雨将苏锦捡回了家。同样的雨夜，苏锦再次见到了那位在自己青春记忆里占比很重的人。苏锦一直看不透沈妄念，只记得小时候她很不喜欢自己喊她姐姐，所以，苏锦一直认为沈妄念很讨厌他。可现在，女人却抱着他呢喃：“小锦，和姐姐一直在一起，好不好。” 苏锦一直认为他会独自过完一辈子，可一些人却强硬地挤进了他的生活…… 初恋抓着他的手，眼神痴情：“我们只是闹了些矛盾，对吗？你不能再抛弃我第二次了……” 银发御姐拥着他的腰，不让他走：“我比她们年轻，比她们有活力，要选就选我啊！你必须选我！”

齐冥南

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人尽可妻

基于人类注意机制的微表情检测方法作者：李婧婷 1 东子朝 1 刘烨 1,2 王甦菁 1,2 庄东哲 3 作者单位： 1. 中国科学院行为科学重点实验室, 北京 100101 2. 中国科学院大学心理学系, 北京 100039 3. 中国人民公安大学公共安全行为科学实验室, 北京 100038 提交时间： 2023-03-28 摘要: 微表情是一种持续时间极短、不易被察觉的面部动作, 揭示了个体的真实情绪, 可以被广泛地应用于谎言识别等领域。而微表情检测的研究受到小样本问题的限制。针对该问题, 本文结合计算机视觉技术与认知心理学实验方法进行探索。首先, 结合眼动技术和呈现-判断范式与阈下情绪启动效应的行为实验范式, 考察微表情识别中选择注意分配的认知机制, 细化人类识别微表情时的特征兴趣区域。其次, 结合人类注意机制, 提出基于自监督学习的多模态微表情检测方法。通过理论和关键技术的突破, 为真实场景下微表情检测的应用奠定基础。 Abstract: Micro-expressions are facial movements that are extremely short and not easily perceived, often generated under high pressure. Micro-expressions can reveal the individual's hidden real emotions and are important non-verbal communication clues, widely used in lies detection and other fields. Due to the difficulty of eliciting, collecting, and labeling micro-expression samples, micro-expression-related research becomes a typical small-sample-size (SSS) problem. In order to enlighten the application of micro-expression analysis technology in complex real-life scenarios such as national security and clinical consultation, this study focuses on the SSS problem and proposes a micro-expression spotting method based on human attention mechanism with multi-branching self-supervised learning through the intersection of computer and psychology. First, this study conducts an exploration related to attentional resources based on the cognitive mechanisms of psychological micro-expressions. A behavioral-experimental paradigm combining eye-movement techniques and a presentation-judgment paradigm with subthreshold emotion priming effects was used to examine the cognitive mechanisms of selective attention allocation in micro-expression recognition and to refine the distinct regions of interest in human recognition of micro-expressions. Thus, the model is effectively and directly enabled to acquire important micro-expression features from the input information. Then the relevant attention modules are further generated from multi-dimensions (time domain, spatial domain, and channel domain) by the deep learning network to improve the performance of the network in extracting micro-expression features with the limited sample size. Second, this study proposes a multi-branching self-supervised learning method based on the human attention mechanism for micro-expression spotting. Training in many unlabeled video samples for the pre-text tasks enables the model to extract features from regions of interest of micro-expressions, including structural and detail features and video dynamic change patterns. Thus, the limitation caused by the SSS problem could be avoided. Finally, the current data released for micro-expressions are video samples and do not include the corresponding depth information. This study will carry out a depth information-based micro-expression spotting method based on the first micro-expression database that includes image depth information being created by our research team. It enables self-supervised learning to learn the corresponding action patterns from the geometric information of the scene. This research will achieve theoretical and technological breakthroughs in the field of automatic micro-expression spotting, improve the accuracy and reliability, and lay the foundation for the application of micro-expression spotting in realistic and complex scenarios. Second, it can achieve the data augmentation of micro-expression samples by mining micro-expression clips in unlabeled videos. Thus, the micro-expression small sample problem could be solved, and the performance improvement of traditional supervised micro-expression spotting methods could be improved. 微表情检测小样本问题人类注意机制自监督学习深度信息期刊：心理科学进展分类：心理学 >> 社会心理学引用： ChinaXiv:202303.09790 (或此版本 ChinaXiv:202303.09790V1 ) DOI:10.3724/SP.J.1042.2022.02143 CSTR:32003.36.ChinaXiv.202303.09790.V1 推荐引用方式：李婧婷,东子朝,刘烨,王甦菁,庄东哲.(2023).基于人类注意机制的微表情检测方法.心理科学进展.doi:10.3724/SP.J.1042.2022.02143 版本历史 [V1] 2023-03-28 00:13:43 ChinaXiv:202303.09790V1 下载全文

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二千五百一十四章

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