时间基础旨在找到目标视频时刻，该目标瞬间与未修剪视频中给定的句子查询相对应。但是，最近的作品发现现有方法遇到了严重的时间偏见问题。这些方法并不是根据训练集中查询的时间偏见过度依赖基于视觉文本语义对齐的目标矩位置。为此，本文提出了一个新颖的培训框架，用于接地模型，以使用洗牌视频解决时间偏见问题而不会失去接地精度。我们的框架介绍了两个辅助任务，即跨模式匹配和时间订单歧视，以促进接地模型训练。跨模式匹配任务利用了洗牌和原始视频之间的内容一致性迫使接地模型以挖掘视觉内容以匹配语义的查询。时间秩序歧视任务利用时间顺序的差异来加强对长期时间环境的理解。关于Charades-STA和活动网字幕的广泛实验证明了我们方法可以减轻对时间偏差的依赖并增强模型对不同时间分布的概括能力的有效性。代码可从https://github.com/haojc/shufflingvideosfortsg获得。

在过去的几十年中，出现了一种趋势，指出在可移动，可编程和可转换机制中利用结构不稳定性。受钢制发夹的启发，我们将面板组件与可靠的结构相结合，并使用半刚性塑料板建造合规的拍打机构，并将其安装在束缚的气动软机器人鱼和无螺旋螺旋式的电动机驱动器上，以展示它的前所未有的优势。设计规则是根据理论和验证提出的。观察到与参考相比，气动鱼的游泳速度提高了两倍，对Untether Fish的进一步研究表明，对于不固定的兼容的游泳运动员，可损坏的速度为2.03 BL/S（43.6 cm/s），优于先前报告的最快的，其幅度为194％。这项工作可能预示着下一代符合下一代机器人技术的结构革命。

本文研究了如何改善接受深入增强学习训练的导航剂的概括性能和学习速度（DRL）。尽管DRL在无机MAP导航中表现出巨大的潜力，但在训练场景中表现良好的DRL代理在不熟悉的情况下经常表现不佳。在这项工作中，我们建议LIDAR读数的表示是代理商效果退化的关键因素，并提出了一种强大的输入预处理（IP）方法来解决此问题。由于这种方法使用适应性的参数倒数函数来预处理激光雷达读数，因此我们将此方法称为IPAPREC及其归一化版本为IPAPRECN。 IPAPREC/IPAPRECN可以突出显示重要的短距离值，并压缩激光扫描中较重要的长距离值的范围，该值很好地解决了由激光扫描的常规表示引起的问题。通过广泛的模拟和现实世界实验来验证它们的高性能。结果表明，与常规方法相比，我们的方法可以大大改善导航剂的概括性能，并大大减少训练时间。

场景图是一个场景的结构化表示，可以清楚地表达场景中对象之间的对象，属性和关系。随着计算机视觉技术继续发展，只需检测和识别图像中的对象，人们不再满足。相反，人们期待着对视觉场景更高的理解和推理。例如，给定图像，我们希望不仅检测和识别图像中的对象，还要知道对象之间的关系（视觉关系检测），并基于图像内容生成文本描述（图像标题）。或者，我们可能希望机器告诉我们图像中的小女孩正在做什么（视觉问题应答（VQA）），甚至从图像中移除狗并找到类似的图像（图像编辑和检索）等。这些任务需要更高水平的图像视觉任务的理解和推理。场景图只是场景理解的强大工具。因此，场景图引起了大量研究人员的注意力，相关的研究往往是跨模型，复杂，快速发展的。然而，目前没有对场景图的相对系统的调查。为此，本调查对现行场景图研究进行了全面调查。更具体地说，我们首先总结了场景图的一般定义，随后对场景图（SGG）和SGG的发电方法进行了全面和系统的讨论，借助于先验知识。然后，我们调查了场景图的主要应用，并汇总了最常用的数据集。最后，我们对场景图的未来发展提供了一些见解。我们相信这将是未来研究场景图的一个非常有帮助的基础。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

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.

This paper presents a practical global optimization algorithm for the K-center clustering problem, which aims to select K samples as the cluster centers to minimize the maximum within-cluster distance. This algorithm is based on a reduced-space branch and bound scheme and guarantees convergence to the global optimum in a finite number of steps by only branching on the regions of centers. To improve efficiency, we have designed a two-stage decomposable lower bound, the solution of which can be derived in a closed form. In addition, we also propose several acceleration techniques to narrow down the region of centers, including bounds tightening, sample reduction, and parallelization. Extensive studies on synthetic and real-world datasets have demonstrated that our algorithm can solve the K-center problems to global optimal within 4 hours for ten million samples in the serial mode and one billion samples in the parallel mode. Moreover, compared with the state-of-the-art heuristic methods, the global optimum obtained by our algorithm can averagely reduce the objective function by 25.8% on all the synthetic and real-world datasets.

It is crucial to evaluate the quality and determine the optimal number of clusters in cluster analysis. In this paper, the multi-granularity characterization of the data set is carried out to obtain the hyper-balls. The cluster internal evaluation index based on hyper-balls(HCVI) is defined. Moreover, a general method for determining the optimal number of clusters based on HCVI is proposed. The proposed methods can evaluate the clustering results produced by the several classic methods and determine the optimal cluster number for data sets containing noises and clusters with arbitrary shapes. The experimental results on synthetic and real data sets indicate that the new index outperforms existing ones.

Score-based diffusion models have captured widespread attention and funded fast progress of recent vision generative tasks. In this paper, we focus on diffusion model backbone which has been much neglected before. We systematically explore vision Transformers as diffusion learners for various generative tasks. With our improvements the performance of vanilla ViT-based backbone (IU-ViT) is boosted to be on par with traditional U-Net-based methods. We further provide a hypothesis on the implication of disentangling the generative backbone as an encoder-decoder structure and show proof-of-concept experiments verifying the effectiveness of a stronger encoder for generative tasks with ASymmetriC ENcoder Decoder (ASCEND). Our improvements achieve competitive results on CIFAR-10, CelebA, LSUN, CUB Bird and large-resolution text-to-image tasks. To the best of our knowledge, we are the first to successfully train a single diffusion model on text-to-image task beyond 64x64 resolution. We hope this will motivate people to rethink the modeling choices and the training pipelines for diffusion-based generative models.

Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML). Feature transformation aims to transform original data to identify an optimal feature space that enhances the performances of a downstream ML model. Existing studies either combines preprocessing, feature selection, and generation skills to empirically transform data, or automate feature transformation by machine intelligence, such as reinforcement learning. However, existing studies suffer from: 1) high-dimensional non-discriminative feature space; 2) inability to represent complex situational states; 3) inefficiency in integrating local and global feature information. To fill the research gap, we formulate the feature transformation task as an iterative, nested process of feature generation and selection, where feature generation is to generate and add new features based on original features, and feature selection is to remove redundant features to control the size of feature space. Finally, we present extensive experiments and case studies to illustrate 24.7\% improvements in F1 scores compared with SOTAs and robustness in high-dimensional data.