Image-based head swapping task aims to stitch a source head to another source body flawlessly. This seldom-studied task faces two major challenges: 1) Preserving the head and body from various sources while generating a seamless transition region. 2) No paired head swapping dataset and benchmark so far. In this paper, we propose an image-based head swapping framework (HS-Diffusion) which consists of a semantic-guided latent diffusion model (SG-LDM) and a semantic layout generator. We blend the semantic layouts of source head and source body, and then inpaint the transition region by the semantic layout generator, achieving a coarse-grained head swapping. SG-LDM can further implement fine-grained head swapping with the blended layout as condition by a progressive fusion process, while preserving source head and source body with high-quality reconstruction. To this end, we design a head-cover augmentation strategy for training and a neck alignment trick for geometric realism. Importantly, we construct a new image-based head swapping benchmark and propose two tailor-designed metrics (Mask-FID and Focal-FID). Extensive experiments demonstrate the superiority of our framework. The code will be available: https://github.com/qinghew/HS-Diffusion.

Speech representation learning has improved both speech understanding and speech synthesis tasks for single language. However, its ability in cross-lingual scenarios has not been explored. In this paper, we extend the pretraining method for cross-lingual multi-speaker speech synthesis tasks, including cross-lingual multi-speaker voice cloning and cross-lingual multi-speaker speech editing. We propose a speech-text joint pretraining framework, where we randomly mask the spectrogram and the phonemes given a speech example and its transcription. By learning to reconstruct the masked parts of the input in different languages, our model shows great improvements over speaker-embedding-based multi-speaker TTS methods. Moreover, our framework is end-to-end for both the training and the inference without any finetuning effort. In cross-lingual multi-speaker voice cloning and cross-lingual multi-speaker speech editing tasks, our experiments show that our model outperforms speaker-embedding-based multi-speaker TTS methods. The code and model are publicly available at PaddleSpeech.

神经辐射场（NERF）在代表具有高分辨率细节和有效记忆的复杂3D场景方面取得了巨大成功。然而，当前基于NERF的姿势估计量没有初始姿势预测，并且在优化过程中易于局部优势。在本文中，我们介绍了纬度：全球定位，具有截短的动态低通滤波器，该过滤器引入了城市规模的NERF中的两阶段定位机制。在识别阶段，我们通过训练有素的NERFS生成的图像来训练回归器，该图像为全球本地化提供了初始值。在姿势优化阶段，我们通过直接优化切线平面上的姿势来最大程度地减少观察到的图像之间的残差和渲染图像。为了避免收敛到局部最优，我们引入了一个截短的动态低通滤波器（TDLF），以进行粗到细小的姿势注册。我们在合成和现实世界中评估了我们的方法，并显示了其在大规模城市场景中高精度导航的潜在应用。代码和数据将在https://github.com/jike5/latitude上公开获取。

近年来，多视图学习迅速发展。尽管许多先前的研究都认为每个实例都出现在所有视图中，但在现实世界应用程序中很常见，从某些视图中丢失实例，从而导致多视图数据不完整。为了解决这个问题，我们提出了一个新型潜在的异质图网络（LHGN），以实现不完整的多视图学习，该学习旨在以灵活的方式尽可能充分地使用多个不完整的视图。通过学习统一的潜在代表，隐含地实现了不同观点之间一致性和互补性之间的权衡。为了探索样本与潜在表示之间的复杂关系，首次提出了邻域约束和视图约束，以构建异质图。最后，为了避免训练和测试阶段之间的任何不一致之处，基于图形学习的分类任务应用了转导学习技术。对现实世界数据集的广泛实验结果证明了我们模型对现有最新方法的有效性。

作为一种新颖的深度学习模型，GCFOREST已被广泛用于各种应用中。但是，当前的GCFOREST多透明扫描会产生许多冗余特征向量，这增加了模型的时间成本。为了筛选冗余特征向量，我们引入了一种用于多透明扫描的哈希筛选机制，并提出了一种称为HW-Forest的模型，该模型采用了两种策略，即哈希筛选和窗口筛选。 HW-Forest采用感知散列算法来计算哈希筛选策略中特征向量之间的相似性，该策略用于删除由多透明扫描产生的冗余特征向量，并可以大大降低时间成本和记忆消耗。此外，我们采用了一种自适应实例筛选策略来提高我们的方法的性能，称为窗口筛选，可以实现更高的精度，而无需在不同数据集上进行超参数调整。我们的实验结果表明，HW-Forest的精度比其他模型更高，并且时间成本也降低。

最近，基于模板的跟踪器已成为领先的跟踪算法，在效率和准确性方面具有希望的性能。然而，查询特征与给定模板之间的相关操作仅利用准确的目标本地化，导致状态估计误差，特别是当目标遭受严重可变形变化时。为了解决这个问题，已经提出了基于分段的跟踪器，以便使用每像素匹配来有效地提高可变形物体的跟踪性能。然而，大多数现有跟踪器仅指初始帧中的目标特征，从而缺乏处理具有挑战性因素的辨别能力，例如，类似的分心，背景杂乱，外观变化等。在此目的，我们提出了一种动态的紧凑型存储器嵌入以增强基于分段的可变形视觉跟踪方法的辨别。具体而言，我们初始化与第一帧中的目标功能嵌入的内存嵌入。在跟踪过程中，与现有内存具有高相关的当前目标特征被更新为在线嵌入的内存。为了进一步提高可变形对象的分割精度，我们采用了点对集的匹配策略来测量像素 - 方向查询特征和整个模板之间的相关性，以捕获更详细的变形信息。关于六个具有挑战性的跟踪基准的广泛评估，包括VOT2016，VOT2018，VOT2019，GOT-10K，TrackingNet和莱斯特展示了我们对近期近似追踪者的方法的优势。此外，我们的方法优于基于出色的基于分段的跟踪器，即DVIS2017基准测试。

Recently, channel attention mechanism has demonstrated to offer great potential in improving the performance of deep convolutional neural networks (CNNs). However, most existing methods dedicate to developing more sophisticated attention modules for achieving better performance, which inevitably increase model complexity.To overcome the paradox of performance and complexity trade-off, this paper proposes an Efficient Channel Attention (ECA) module, which only involves a handful of parameters while bringing clear performance gain. By dissecting the channel attention module in SENet, we empirically show avoiding dimensionality reduction is important for learning channel attention, and appropriate cross-channel interaction can preserve performance while significantly decreasing model complexity. Therefore, we propose a local crosschannel interaction strategy without dimensionality reduction, which can be efficiently implemented via 1D convolution. Furthermore, we develop a method to adaptively select kernel size of 1D convolution, determining coverage of local cross-channel interaction. The proposed ECA module is efficient yet effective, e.g., the parameters and computations of our modules against backbone of ResNet50 are 80 vs. 24.37M and 4.7e-4 GFLOPs vs. 3.86 GFLOPs, respectively, and the performance boost is more than 2% in terms of Top-1 accuracy. We extensively evaluate our ECA module on image classification, object detection and instance segmentation with backbones of ResNets and MobileNetV2. The experimental results show our module is more efficient while performing favorably against its counterparts.

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.

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.

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.