在实际人群计算应用程序中，图像中的人群密度差异很大。当面对密度变化时，人类倾向于在低密度区域定位和计数目标，并推理高密度区域的数量。我们观察到，CNN使用固定大小的卷积内核专注于局部信息相关性，而变压器可以通过使用全球自我注意机制有效地提取语义人群信息。因此，CNN可以在低密度区域中准确定位和估计人群，而在高密度区域中很难正确感知密度。相反，变压器在高密度区域具有很高的可靠性，但未能在稀疏区域定位目标。 CNN或变压器都无法很好地处理这种密度变化。为了解决此问题，我们提出了一个CNN和变压器自适应选择网络（CTASNET），该网络可以自适应地为不同密度区域选择适当的计数分支。首先，CTASNET生成CNN和变压器的预测结果。然后，考虑到CNN/变压器适用于低/高密度区域，密度引导的自适应选择模块被设计为自动结合CNN和Transformer的预测。此外，为了减少注释噪声的影响，我们引入了基于Correntropy的最佳运输损失。对四个挑战的人群计数数据集进行了广泛的实验，已经验证了该方法。

背景噪声和规模变化是人群计数中长期以来已经认识到的常见问题。人类瞥见人群的形象，立即知道人类的大概数量，以及他们通过关注的人群地区和人群地区的拥塞程度，并具有全球接收领域。因此，在本文中，我们通过对人类自上而下的视觉感知机制进行建模，提出了一个具有称为RANET的区域感知块的新型反馈网络。首先，我们介绍了一个反馈体系结构，以生成优先级地图，这些图提供了输入图像中候选人人群区域的先验。先验使Ranet更加关注人群地区。然后，我们设计了可以通过全局接受字段自适应地将上下文信息编码为输入图像的区域感知块。更具体地说，我们以列向量的形式扫描整个输入图像及其优先级图，以获得相关矩阵估计其相似性。获得的相关矩阵将用于建立像素之间的全球关系。我们的方法在几个公共数据集上优于最先进的人群计数方法。

Robot navigation in dynamic environments shared with humans is an important but challenging task, which suffers from performance deterioration as the crowd grows. In this paper, multi-subgoal robot navigation approach based on deep reinforcement learning is proposed, which can reason about more comprehensive relationships among all agents (robot and humans). Specifically, the next position point is planned for the robot by introducing history information and interactions in our work. Firstly, based on subgraph network, the history information of all agents is aggregated before encoding interactions through a graph neural network, so as to improve the ability of the robot to anticipate the future scenarios implicitly. Further consideration, in order to reduce the probability of unreliable next position points, the selection module is designed after policy network in the reinforcement learning framework. In addition, the next position point generated from the selection module satisfied the task requirements better than that obtained directly from the policy network. The experiments demonstrate that our approach outperforms state-of-the-art approaches in terms of both success rate and collision rate, especially in crowded human environments.

为了解决控制循环的耦合问题和多输入多输出（MIMO）PID控制系统中的自适应参数调谐问题，基于深度加强学习（RL）和Lyapunov-提出了一种自适应LSAC-PID算法本文基于奖励塑造。对于复杂和未知的移动机器人控制环境，首先呈现了基于RL的MIMO PID混合控制策略。根据移动机器人的动态信息和环境反馈，RL代理可以实时输出最佳MIMO PID参数，而不知道数学模型和解耦多个控制回路。然后，提高RL的收敛速度和移动机器人的稳定性，基于Lyapunov理论和基于潜在的奖励整形方法提出了一种基于Lyapunov的奖励塑形软演员 - 评论仪（LSAC）算法。算法的收敛性和最优性在于软政策迭代的策略评估和改进步骤。此外，对于线路跟随机器人，改进了该区域生长方法，以适应叉和环境干扰的影响。通过比较，测试和交叉验证，仿真和实际实验结果均显示出所提出的LSAC-PID调谐算法的良好性能。

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.