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我的熟练度能长生

叶凡穿越到洪荒之中。好消息：获得一门玄阴功法，接触女仙就可不断变强。坏消息：这可是洪荒！哪里有温柔可人的女仙？！那都是暴力倾向，一言不合，直接一道眼神便可灭掉自己的女仙啊！叶凡绝望了，这功法跟废了有什么区别？就在这时，一尊女仙“尸体”从天上掉下来，神魂俱灭，只剩下一丝真灵意识，仿佛植物人一般。叶凡狂喜：天不绝我也！……我是女娲的恶尸。获得了造化鼎，造化出自我神智。后来，与本尊起了争执，受到重伤，只剩下一丝真灵不灭。却没想到，有个小修士开始照顾起我的肉身，不断灌输先天灵气，滋养神魂。这……很不错。但是，为什么他总是要贴着本座！待本座醒来之后，定将他碎尸万段！无数年后，女娲恶尸醒来，神性中多了一道人性。叶凡：完了，吾命休矣！却见，那位高冷尊贵的女仙，一把拽住他的衣袖，耳尖微红：“再贴一会儿。”“本座还没恢复，不……不许走！”叶凡：“？？？”说好的洪荒女仙凶残呢？女娲恶尸：嘻嘻，本座承认贴贴真香！我还想要~ 版权：创世中文网

炩岚

商城

大明：我老朱家大孙，武宗朱雄英

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

二喜综漫

北域大营

【微克苏鲁+万诡迷+雄竞修罗场+1vn+坏女人+玛丽苏】沈知安经历了两次穿越，第一次穿越到诡异世界，经过摸爬滚打之后，靠【吞噬】异能统治该世界。第二次穿越终于到了正常的人类世界，有正常的爸爸妈妈，还有个很负责的哥哥，就在沈知安准备享受生活的时候。她上个世界的下属出现在她面前，激动得浑身颤抖，露出痴迷喜悦爱慕交织在一起的表情。 ”王，王，我挚爱的王，终于，终于找到您了!” 沈知安：…… 这个世界怎么能出这么大漏洞! —— 灵异事件频发，出现的怪物似乎在找它们的王，或者想要复活它们的王。那它们的王究竟在哪儿呢？沈知安：不知道不清楚不明白，勿q。避雷： 1.女主不是好人。 2.所有出现的怪物都疯狂地爱着女主 3.玛丽苏爽文，有逻辑但不多就这样希望大家看的愉快呀!

奶酪蜜桃子

工具书

穿梭诸天万域

【大学校园｜暗恋成真｜浪子回头｜追妻甜宠】孟诗意有个暗恋多年的人，他是哥哥的好朋友。贺西楼，帝大出了名的桀骜不驯、随性放荡，谈过的前女友能绕学校一圈。起初，他笑得漫不经心：“放心，朋友的妹妹，我不碰～” 后来，他把人摁在怀里，嗓音又撩又欲：“诗意妹妹，占我便宜，是不是得对我负责？” 孟诗意仍然不敢相信，自己真的会和贺西楼谈恋爱，这是她遥不可及的少女梦。就在她被撩得脸红心跳时。却意外听到贺西楼淡漠的声音：“陪小姑娘玩玩而已，压根没动真心。” 孟诗意这才明白，浪子怎会回头。可后来，贺西楼嫉妒发疯。深夜把她堵在墙角，双眼微红地吻住她： “宝宝，别分手，再给我一次机会……” “下辈子换我暗恋你，好不好？” （双处，高糖，甜宠苏撩～）

灵感菇

碾压

天烬云殇

说起部落这个词，兰州资源环境职业技术大学人脑子里立刻想起了万里草原，低头见牛羊的游牧景象，实际上，在关外的大部分女真部落都是从事农耕和渔猎，他们养猪种地，捕鱼打猎，和关内的农民相比，他们因为捕鱼打猎得到了许多军事化和半军事化的协作和训练

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大明：我老朱家大孙，武宗朱雄英

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穿梭诸天万域

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