我们提出了一种小型任务，可以衡量人们如何基于观察单个（实验1）或几个（实验2）对象对之间的因果相互作用来概括物体的因果动力。我们提出了一种计算建模框架，可以在我们的任务环境中综合人类的泛化模式，并阐明人们如何有效地浏览可能的因果函数和类别的组成空间。我们的建模框架结合了使用代理和收件人对象的特征和关系的因果函数发生器，以及贝叶斯非参数推断过程，以控制基于相似性的概念。我们的模型具有自然的“资源合理的”变体，可以在描述参与者时优于一个天真的贝叶斯账户，特别是在我们的行为实验中再现透明阶效应和因果不对称。我们认为，该建模框架为真实世界因果概念提供了计算上的合理机制。

在本文中，我们专注于分析和改进视觉变压器自我发项层的辍学技术，这很重要，同时令人惊讶地被先前的作品忽略了。特别是，我们对三个核心问题进行研究：首先，自我发挥层的下降是什么？不同于文献中的注意力重量不同，我们建议在注意矩阵计算之前向前移动辍学操作，并将钥匙设置为辍学单元，从而产生一种新颖的辍学效果。从理论上讲，我们验证了该方案是否有助于保持注意力重量的正则化和概率特征，从而减轻了过度拟合问题的特定模式，并增强了模型以捕获重要信息；第二，如何在连续层中安排下降比？与利用所有层的恒定下降比相反，我们提出了新的减少时间表，该计划逐渐降低了沿自我注意力层的堆叠比率。我们通过实验验证提出的时间表可以避免在低水平特征中过度贴合，并且在高级语义中缺失，从而提高了模型训练的稳健性和稳定性；第三，是否需要执行结构化辍学操作为CNN？我们尝试基于补丁的辍学操作区块，发现CNN的这种有用的技巧对于VIT并不是必需的。考虑到以上三个问题的探索，我们提出了一种新颖的Dropkey方法，该方法将密钥视为下降单元和利用下降比的减少时间表，以一般方式改善VIT。全面的实验证明了Dropkey对各种VIT体系结构的有效性，\ Emph {e.g。} T2T和Volo以及各种视觉任务，\ Emph {e.g。}，图像分类，对象检测，人类对象相互作用和人体形状检测和人体形状恢复。代码将在接受后发布。

大型预先接受的变压器的语言模型，如BERT大大改变了自然语言处理（NLP）字段。我们展示了对最近的工作的调查，这些工作使用这些大型语言模型通过预先训练，提示或文本生成方法来解决NLP任务。我们还提出了使用预先训练的语言模型来生成培训增强或其他目的的数据的方法。我们在讨论有关未来研究的局限性和建议方向的结论。

音频数据增强是培训深度神经网络以解决音频分类任务的关键步骤。在本文中，我们在Matlab中引入了一个新型音频数据增强库的录音机。我们为RAW音频数据提供了15种不同的增强算法，8用于频谱图。我们有效地实施了几种增强技术，其有用性在文献中被广泛证明。据我们所知，这是最大的Matlab音频数据增强图书馆可自由使用。我们验证了我们在ESC-50数据集上评估它们的算法的效率。可以在https://github.com/lorisnanni/audiogmenter下载工具箱及其文档。

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.

As one of the most important psychic stress reactions, micro-expressions (MEs), are spontaneous and transient facial expressions that can reveal the genuine emotions of human beings. Thus, recognizing MEs (MER) automatically is becoming increasingly crucial in the field of affective computing, and provides essential technical support in lie detection, psychological analysis and other areas. However, the lack of abundant ME data seriously restricts the development of cutting-edge data-driven MER models. Despite the recent efforts of several spontaneous ME datasets to alleviate this problem, it is still a tiny amount of work. To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7,526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years. Afterwards, we adopt four classical spatiotemporal feature learning models on DFME to perform MER experiments to objectively verify the validity of DFME dataset. In addition, we explore different solutions to the class imbalance and key-frame sequence sampling problems in dynamic MER respectively on DFME, so as to provide a valuable reference for future research. The comprehensive experimental results show that our DFME dataset can facilitate the research of automatic MER, and provide a new benchmark for MER. DFME will be published via https://mea-lab-421.github.io.

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.

Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this paper, we propose a Unified Timeline Summarizer (UTS) that can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information remained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that UTS achieves state-of-the-art performance in terms of both automatic and human evaluations.

For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion) batteries, many models have been established to characterize their degradation process. The existing empirical or physical models can reveal important information regarding the degradation dynamics. However, there is no general and flexible methods to fuse the information represented by those models. Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical or physical dynamic models with data-driven models. To take full advantage of various information sources, we propose a model fusion scheme based on PINN. It is implemented by developing a semi-empirical semi-physical Partial Differential Equation (PDE) to model the degradation dynamics of Li-ion-batteries. When there is little prior knowledge about the dynamics, we leverage the data-driven Deep Hidden Physics Model (DeepHPM) to discover the underlying governing dynamic models. The uncovered dynamics information is then fused with that mined by the surrogate neural network in the PINN framework. Moreover, an uncertainty-based adaptive weighting method is employed to balance the multiple learning tasks when training the PINN. The proposed methods are verified on a public dataset of Li-ion Phosphate (LFP)/graphite batteries.