Video semantic segmentation (VSS) is beneficial for dealing with dynamic scenes due to the continuous property of the real-world environment. On the one hand, some methods alleviate the predicted inconsistent problem between continuous frames. On the other hand, other methods employ the previous frame as the prior information to assist in segmenting the current frame. Although the previous methods achieve superior performances on the independent and identically distributed (i.i.d) data, they can not generalize well on other unseen domains. Thus, we explore a new task, the video generalizable semantic segmentation (VGSS) task that considers both continuous frames and domain generalization. In this paper, we propose a class-wise non-salient region generalized (CNSG) framework for the VGSS task. Concretely, we first define the class-wise non-salient feature, which describes features of the class-wise non-salient region that carry more generalizable information. Then, we propose a class-wise non-salient feature reasoning strategy to select and enhance the most generalized channels adaptively. Finally, we propose an inter-frame non-salient centroid alignment loss to alleviate the predicted inconsistent problem in the VGSS task. We also extend our video-based framework to the image-based generalizable semantic segmentation (IGSS) task. Experiments demonstrate that our CNSG framework yields significant improvement in the VGSS and IGSS tasks.

The development of deep learning models in medical image analysis is majorly limited by the lack of large-sized and well-annotated datasets. Unsupervised learning does not require labels and is more suitable for solving medical image analysis problems. However, most of the current unsupervised learning methods need to be applied to large datasets. To make unsupervised learning applicable to small datasets, we proposed Swin MAE, which is a masked autoencoder with Swin Transformer as its backbone. Even on a dataset of only a few thousand medical images and without using any pre-trained models, Swin MAE is still able to learn useful semantic features purely from images. It can equal or even slightly outperform the supervised model obtained by Swin Transformer trained on ImageNet in terms of the transfer learning results of downstream tasks. The code will be publicly available soon.

The recent trend in multiple object tracking (MOT) is jointly solving detection and tracking, where object detection and appearance feature (or motion) are learned simultaneously. Despite competitive performance, in crowded scenes, joint detection and tracking usually fail to find accurate object associations due to missed or false detections. In this paper, we jointly model counting, detection and re-identification in an end-to-end framework, named CountingMOT, tailored for crowded scenes. By imposing mutual object-count constraints between detection and counting, the CountingMOT tries to find a balance between object detection and crowd density map estimation, which can help it to recover missed detections or reject false detections. Our approach is an attempt to bridge the gap of object detection, counting, and re-Identification. This is in contrast to prior MOT methods that either ignore the crowd density and thus are prone to failure in crowded scenes, or depend on local correlations to build a graphical relationship for matching targets. The proposed MOT tracker can perform online and real-time tracking, and achieves the state-of-the-art results on public benchmarks MOT16 (MOTA of 77.6), MOT17 (MOTA of 78.0%) and MOT20 (MOTA of 70.2%).

The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems. To accelerate the research on AI security, the Artificial Intelligence Security Competition (AISC) was organized by the Zhongguancun Laboratory, China Industrial Control Systems Cyber Emergency Response Team, Institute for Artificial Intelligence, Tsinghua University, and RealAI as part of the Zhongguancun International Frontier Technology Innovation Competition (https://www.zgc-aisc.com/en). The competition consists of three tracks, including Deepfake Security Competition, Autonomous Driving Security Competition, and Face Recognition Security Competition. This report will introduce the competition rules of these three tracks and the solutions of top-ranking teams in each track.

Existing correspondence datasets for two-dimensional (2D) cartoon suffer from simple frame composition and monotonic movements, making them insufficient to simulate real animations. In this work, we present a new 2D animation visual correspondence dataset, AnimeRun, by converting open source three-dimensional (3D) movies to full scenes in 2D style, including simultaneous moving background and interactions of multiple subjects. Our analyses show that the proposed dataset not only resembles real anime more in image composition, but also possesses richer and more complex motion patterns compared to existing datasets. With this dataset, we establish a comprehensive benchmark by evaluating several existing optical flow and segment matching methods, and analyze shortcomings of these methods on animation data. Data, code and other supplementary materials are available at https://lisiyao21.github.io/projects/AnimeRun.

Robotic force-based compliance control is a preferred approach to achieve high-precision assembly tasks. When the geometric features of assembly objects are asymmetric or irregular, reinforcement learning (RL) agents are gradually incorporated into the compliance controller to adapt to complex force-pose mapping which is hard to model analytically. Since force-pose mapping is strongly dependent on geometric features, a compliance controller is only optimal for current geometric features. To reduce the learning cost of assembly objects with different geometric features, this paper is devoted to answering how to reconfigure existing controllers for new assembly objects with different geometric features. In this paper, model-based parameters are first reconfigured based on the proposed Equivalent Theory of Compliance Law (ETCL). Then the RL agent is transferred based on the proposed Weighted Dimensional Policy Distillation (WDPD) method. The experiment results demonstrate that the control reconfiguration method costs less time and achieves better control performance, which confirms the validity of proposed methods.

我们提出了针对微小神经网络的域概括（DG）的系统研究，这个问题对于机上机器学习应用至关重要，但在研究仅针对大型模型的文献中被忽略了。微小的神经网络具有较少的参数和较低的复杂性，因此不应以与DG应用的大型同行相同的方式进行训练。我们发现知识蒸馏是解决问题的有力候选者：它优于使用具有较大利润的大型模型开发的最先进的DG方法。此外，我们观察到，与域移动有关的测试数据上的教师学生绩效差距大于分布数据的绩效差距。为了改善微小神经网络而不增加部署成本的DG，我们提出了一个简单的想法，称为分布外知识蒸馏（OKD），该想法旨在教导学生如何处理（综合）分发数据和分布数据和被证明是解决问题的有前途的框架。我们还为创建DG数据集的可扩展方法（在上下文中称为域移动（DOSCO））提供了可扩展的方法，该数据可以在不大量努力的情况下按大规模应用大量数据。代码和模型以\ url {https://github.com/kaiyangzhou/on-device-dg}发布。

本文探讨了管状结构提取任务的点集表示。与传统的掩码表示相比，点集表示享有其灵活性和表示能力，这不会受到固定网格作为掩模的限制。受此启发，我们提出了PointCatter，这是管状结构提取任务的分割模型的替代方法。PointCatter将图像分为散射区域，并对每个散点区域预测点。我们进一步提出了基于贪婪的区域的两分匹配算法，以端到端训练网络。我们在四个公共管状数据集上基准测试了点刻表，并且有关管状结构分割和中心线提取任务的广泛实验证明了我们方法的有效性。代码可在https://github.com/zhangzhao2022/pointscatter上找到。

自我模型是一种过程，例如动物或机器等代理商学会创建自己动态的预测模型。一旦被捕获，这种自模型就可以允许代理使用自我模型在内部计划和评估各种潜在行为，而不是使用昂贵的物理实验。在这里，我们量化了这种自模型对机器人的复杂性的好处。我们发现与直接学习基线相比，机器人拥有的自由度数量与自模型的附加值之间的R2 = 0.90相关性。这一结果可能有助于激发日益复杂的机器人系统中的自我建模，并阐明动物和人类自我模型的起源，并最终自我意识。

数据增强（DA） - 在原始培训集中生成额外的培训样本 - 在当今无偏见的VQA模型中已广泛使用，以减轻语言偏见。当前的主流DA策略是基于合成的方法，它通过编辑某些视觉区域/单词或从头开始重新生成它们来合成新样本。但是，这些合成样品始终是不自然的和错误的。为了避免此问题，最近的DA工作通过随机配对原始图像和其他人为编写的问题来构成新的增强样品。不幸的是，为了确保增强样品具有合理的基础答案，他们手动为几种问题类型设计了一套启发式规则，这极大地限制了其概括能力。为此，我们提出了一种新的基于知识蒸馏的数据增强，以称为Kddaug。具体而言，我们首先放松合理图像问题对的要求，可以轻松地应用于任何问题类型。然后，我们设计了一个基于知识蒸馏（KD）的答案分配，以生成所有组成图像问题对的伪答案，这些答案对内域和分布外设置都很健壮。由于Kddaug是一种模型不合时宜的DA策略，因此可以将其无缝合并到任何VQA架构中。关于多个骨干和基准测试的大量消融研究证明了Kddaug的有效性和概括能力。