基于对比度学习的基于自我监督的骨架识别引起了很多关注。最近的文献表明，数据增强和大量对比度对对于学习此类表示至关重要。在本文中，我们发现，基于正常增强的直接扩展对对比对的表现有限，因为随着培训的进展，对比度对从正常数据增强到损失的贡献越小。因此，我们深入研究了对比对比对的，以进行对比学习。由混合增强策略的成功激励，通过综合新样本来改善许多任务的执行，我们提出了Skelemixclr：一种与时空的学习框架，具有时空骨架混合增强（Skelemix），以补充当前的对比样品，以补充当前的对比样品。首先，Skelemix利用骨架数据的拓扑信息将两个骨骼序列混合在一起，通过将裁切的骨骼片段（修剪视图）与其余的骨架序列（截断视图）随机梳理。其次，应用时空掩码池在特征级别上分开这两个视图。第三，我们将对比度对与这两种观点扩展。 SkelemixClr利用修剪和截断的视图来提供丰富的硬对比度对，因为它们由于图形卷积操作而涉及彼此的某些上下文信息，这使模型可以学习更好的运动表示以进行动作识别。在NTU-RGB+D，NTU120-RGB+D和PKU-MMD数据集上进行了广泛的实验表明，SkelemixClr实现了最先进的性能。代码可在https://github.com/czhaneva/skelemixclr上找到。

复杂的水下环境为物体检测带来了新的挑战，例如未平衡的光条件，低对比度，阻塞和水生生物的模仿。在这种情况下，水下相机捕获的物体将变得模糊，并且通用探测器通常会在这些模糊的物体上失败。这项工作旨在从两个角度解决问题：不确定性建模和艰难的例子采矿。我们提出了一个名为Boosting R-CNN的两阶段水下检测器，该检测器包括三个关键组件。首先，提出了一个名为RetinArpn的新区域建议网络，该网络提供了高质量的建议，并考虑了对象和IOU预测，以确定对象事先概率的不确定性。其次，引入了概率推理管道，以结合第一阶段的先验不确定性和第二阶段分类评分，以模拟最终检测分数。最后，我们提出了一种名为Boosting Reweighting的新的硬示例挖掘方法。具体而言，当区域提案网络误认为样品的对象的事先概率时，提高重新加权将在训练过程中增加R-CNN头部样品的分类损失，同时减少具有准确估计的先验的简易样品丢失。因此，可以在第二阶段获得强大的检测头。在推理阶段，R-CNN具有纠正第一阶段的误差以提高性能的能力。在两个水下数据集和两个通用对象检测数据集上进行的全面实验证明了我们方法的有效性和鲁棒性。

自我注意力是深度学习中最成功的设计之一，它可以根据注意矩阵计算不同令牌的相似性并重建功能。自我注意力最初是为NLP设计的，在计算机视觉中也很受欢迎，可以分为像素级的注意力和贴片级别的关注。在对象检测中，ROI功能可以看作是基本特征图的补丁。本文旨在将注意模块应用于ROI功能以提高性能。我们选择了外部注意模块，而不是采用原始的自我发项模块，这是一种具有减少参数的修改后的自我发明。借助提出的双头结构和位置编码模块，我们的方法可以在对象检测中实现有希望的性能。综合实验表明，它实现了有希望的性能，尤其是在水下对象检测数据集中。该代码将在：https：//github.com/zsyasd/excavating-roi-prestition-for-under-underwater-object-detection中避免以下内容。

被遮挡的人重新识别是一个具有挑战性的任务，因为某些场景中的某些障碍（例如树木，汽车和行人）封闭人体部分。一些现有的姿势引导方法通过根据图形匹配对准身体部位来解决这个问题，但这些基于图的方法不直观和复杂。因此，我们提出了一种基于变压器的姿态引导特征解除留出（PFD）方法，通过利用姿势信息来清楚地解散语义部件（例如人体或关节部件）并相应地选择性地匹配非封闭部分。首先，视觉变压器（VIV）用于提取具有强大功能的贴片功能。其次，为了从补丁信息预先解散姿势信息，匹配和分配机制在姿势引导特征聚合（PFA）模块中利用。第三，在变压器解码器中引入了一组学习的语义视图，以隐式增强解除戒备的身体部位特征。然而，没有额外监督，那些语义视图并不保证与身体相关。因此，提出了姿势视图匹配（PVM）模块以明确匹配可见的身体部位并自动分离遮挡功能。第四，为了更好地防止闭塞的干扰，我们设计了一个姿势引导的推动损失，强调了可见的身体部位的特征。对于两个任务（封闭和整体RE-ID）的五个具有挑战性的数据集进行了广泛的实验表明，我们提出的PFD具有优越的承诺，这对最先进的方法表现了有利的方法。代码可在https://github.com/wangtaoas/pfd_net上获得

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

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.

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.

Temporal sentence grounding (TSG) aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning. However, we argue that these methods have overlooked two indispensable issues: 1) Boundary-bias: The annotated target segment generally refers to two specific frames as corresponding start and end timestamps. The video downsampling process may lose these two frames and take the adjacent irrelevant frames as new boundaries. 2) Reasoning-bias: Such incorrect new boundary frames also lead to the reasoning bias during frame-query interaction, reducing the generalization ability of model. To alleviate above limitations, in this paper, we propose a novel Siamese Sampling and Reasoning Network (SSRN) for TSG, which introduces a siamese sampling mechanism to generate additional contextual frames to enrich and refine the new boundaries. Specifically, a reasoning strategy is developed to learn the inter-relationship among these frames and generate soft labels on boundaries for more accurate frame-query reasoning. Such mechanism is also able to supplement the absent consecutive visual semantics to the sampled sparse frames for fine-grained activity understanding. Extensive experiments demonstrate the effectiveness of SSRN on three challenging datasets.

Human parsing aims to partition humans in image or video into multiple pixel-level semantic parts. In the last decade, it has gained significantly increased interest in the computer vision community and has been utilized in a broad range of practical applications, from security monitoring, to social media, to visual special effects, just to name a few. Although deep learning-based human parsing solutions have made remarkable achievements, many important concepts, existing challenges, and potential research directions are still confusing. In this survey, we comprehensively review three core sub-tasks: single human parsing, multiple human parsing, and video human parsing, by introducing their respective task settings, background concepts, relevant problems and applications, representative literature, and datasets. We also present quantitative performance comparisons of the reviewed methods on benchmark datasets. Additionally, to promote sustainable development of the community, we put forward a transformer-based human parsing framework, providing a high-performance baseline for follow-up research through universal, concise, and extensible solutions. Finally, we point out a set of under-investigated open issues in this field and suggest new directions for future study. We also provide a regularly updated project page, to continuously track recent developments in this fast-advancing field: https://github.com/soeaver/awesome-human-parsing.

A storyboard is a roadmap for video creation which consists of shot-by-shot images to visualize key plots in a text synopsis. Creating video storyboards however remains challenging which not only requires association between high-level texts and images, but also demands for long-term reasoning to make transitions smooth across shots. In this paper, we propose a new task called Text synopsis to Video Storyboard (TeViS) which aims to retrieve an ordered sequence of images to visualize the text synopsis. We construct a MovieNet-TeViS benchmark based on the public MovieNet dataset. It contains 10K text synopses each paired with keyframes that are manually selected from corresponding movies by considering both relevance and cinematic coherence. We also present an encoder-decoder baseline for the task. The model uses a pretrained vision-and-language model to improve high-level text-image matching. To improve coherence in long-term shots, we further propose to pre-train the decoder on large-scale movie frames without text. Experimental results demonstrate that our proposed model significantly outperforms other models to create text-relevant and coherent storyboards. Nevertheless, there is still a large gap compared to human performance suggesting room for promising future work.