多次人类解析的现有方法通常采用两阶段的策略（通常是自下而上和自下而上），这遭受了对先前检测的强烈依赖，或者在集体后过程中高度计算的冗余。在这项工作中，我们使用代表性零件（称为Repparser）提出了一个端到端的多个人类解析框架。与主流方法不同，repparser以新的单阶段的方式解决了多个人的解析，而无需诉诸于人的检测或组后。为此，repparser将解析管道解析为实例感知的内核产生和部分意识到的人类解析，并将其分解为部分。分别负责实例分离和特定于实例的部分分割。特别是，我们通过代表性部分赋予解析管道的能力，因为它们的特征是通过实例感知关键点，并且可以用来动态解析每个人的实例。具体而言，代表性零件是通过共同定位实例中心和估计身体部位区域的关键来获得的。之后，我们通过代表性部分动态预测实例感知的卷积内核，从而将人零件的上下文编码为每个负责将图像特征施放为实例特定表示的内核。furthermore。采用多支出结构来分割每个实例 - 特定的表示单独的部分分割的几个部分感知表示。这样，以代表性零件的指导，重新集中在人实例上，并直接为每个人实例输出解析结果，从而消除了先前检测或发布的要求-grouping。在两个具有挑战性的基准上进行的扩展实验表明，我们提出的repparser是一个简单而有效的框架，并取得了竞争性的表现。

零件级别的属性解析是一项基本但具有挑战性的任务，它需要区域级的视觉理解以提供可解释的身体部位细节。大多数现有方法通过添加具有属性预测头到两阶段检测器的区域卷积神经网络（RCNN）来解决此问题，其中从本地零件框中确定了身体部位的属性。但是，具有极限视觉线索的本地零件框（即仅零件外观）会导致不满意的解析结果，因为身体部位的属性高度依赖于它们之间的全面关系。在本文中，我们建议通过利用丰富的知识来识别嵌入式RCNN（KE-RCNN）来识别属性-hip）和显式知识（例如，``短裤''的一部分不能具有``连帽衫''或``衬里''的属性）。具体而言，KE-RCNN由两个新型组件，即基于隐式知识的编码器（IK-en）和基于知识的显式解码器（EK-DE）组成。前者旨在通过将部分的关系上下文编码到部分框中来增强零件级的表示，而后者则建议通过有关\ textit {part-attribute}关系的先验知识的指导来解码属性。这样，KE-RCNN就是插件播放，可以集成到任何两阶段检测器中，例如attribute-rcnn，cascade-rcnn，基于HRNET的RCNN和基于Swintransformer的RCNN。在两个具有挑战性的基准上进行的广泛实验，例如Fashionpedia和Kinetics-TPS，证明了KE-RCNN的有效性和概括性。特别是，它比所有现有方法都取得了更高的改进，在时尚Pedia上达到了3％的AP，而动力学TPS的ACC约为4％。

部分一级的行动解析针对部分状态解析为影片提升动作识别。尽管在视频分类研究领域戏剧性的进展，面临的社会的一个严重问题是，人类活动的详细了解被忽略。我们的动机是，解析人的行动需要建立模型，专注于特定的问题。我们提出了一个简单而有效的方法，迎刃而解命名解析动作（DAP）。具体来说，我们划分部分一级行动解析为三个阶段：1）人的检测，当一个人检测器采用检测从影片的所有人员以及进行实例级动作识别; 2）部分解析，其中解析部分模型提出了识别来自检测到的人物图像人类份;和3）动作解析，其中，多模态动作解析网络用于分析动作类别调节对从先前阶段获得的所有检测结果。随着应用这三大车型，我们DAP的方法记录$ 0.605 $得分的全球平均在2021动力学-TPS挑战。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

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.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

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.

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.

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.