Marine waves significantly disturb the unmanned surface vehicle (USV) motion. An unmanned aerial vehicle (UAV) can hardly land on a USV that undergoes irregular motion. An oversized landing platform is usually necessary to guarantee the landing safety, which limits the number of UAVs that can be carried. We propose a landing system assisted by tether and robot manipulation. The system can land multiple UAVs without increasing the USV's size. An MPC controller stabilizes the end-effector and tracks the UAVs, and an adaptive estimator addresses the disturbance caused by the base motion. The working strategy of the system is designed to plan the motion of each device. We have validated the manipulator controller through simulations and well-controlled indoor experiments. During the field tests, the proposed system caught and placed the UAVs when the disturbed USV roll range was approximately 12 degrees.

For sequence generation, both autoregressive models and non-autoregressive models have been developed in recent years. Autoregressive models can achieve high generation quality, but the sequential decoding scheme causes slow decoding speed. Non-autoregressive models accelerate the inference speed with parallel decoding, while their generation quality still needs to be improved due to the difficulty of modeling multi-modalities in data. To address the multi-modality issue, we propose Diff-Glat, a non-autoregressive model featured with a modality diffusion process and residual glancing training. The modality diffusion process decomposes the modalities and reduces the modalities to learn for each transition. And the residual glancing sampling further smooths the modality learning procedures. Experiments demonstrate that, without using knowledge distillation data, Diff-Glat can achieve superior performance in both decoding efficiency and accuracy compared with the autoregressive Transformer.

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.

Motion prediction is highly relevant to the perception of dynamic objects and static map elements in the scenarios of autonomous driving. In this work, we propose PIP, the first end-to-end Transformer-based framework which jointly and interactively performs online mapping, object detection and motion prediction. PIP leverages map queries, agent queries and mode queries to encode the instance-wise information of map elements, agents and motion intentions, respectively. Based on the unified query representation, a differentiable multi-task interaction scheme is proposed to exploit the correlation between perception and prediction. Even without human-annotated HD map or agent's historical tracking trajectory as guidance information, PIP realizes end-to-end multi-agent motion prediction and achieves better performance than tracking-based and HD-map-based methods. PIP provides comprehensive high-level information of the driving scene (vectorized static map and dynamic objects with motion information), and contributes to the downstream planning and control. Code and models will be released for facilitating further research.

Learning semantic-rich representations from raw unlabeled time series data is critical for downstream tasks such as classification and forecasting. Contrastive learning has recently shown its promising representation learning capability in the absence of expert annotations. However, existing contrastive approaches generally treat each instance independently, which leads to false negative pairs that share the same semantics. To tackle this problem, we propose MHCCL, a Masked Hierarchical Cluster-wise Contrastive Learning model, which exploits semantic information obtained from the hierarchical structure consisting of multiple latent partitions for multivariate time series. Motivated by the observation that fine-grained clustering preserves higher purity while coarse-grained one reflects higher-level semantics, we propose a novel downward masking strategy to filter out fake negatives and supplement positives by incorporating the multi-granularity information from the clustering hierarchy. In addition, a novel upward masking strategy is designed in MHCCL to remove outliers of clusters at each partition to refine prototypes, which helps speed up the hierarchical clustering process and improves the clustering quality. We conduct experimental evaluations on seven widely-used multivariate time series datasets. The results demonstrate the superiority of MHCCL over the state-of-the-art approaches for unsupervised time series representation learning.

This paper is a technical overview of DeepMind and Google's recent work on reinforcement learning for controlling commercial cooling systems. Building on expertise that began with cooling Google's data centers more efficiently, we recently conducted live experiments on two real-world facilities in partnership with Trane Technologies, a building management system provider. These live experiments had a variety of challenges in areas such as evaluation, learning from offline data, and constraint satisfaction. Our paper describes these challenges in the hope that awareness of them will benefit future applied RL work. We also describe the way we adapted our RL system to deal with these challenges, resulting in energy savings of approximately 9% and 13% respectively at the two live experiment sites.

Person re-identification plays a significant role in realistic scenarios due to its various applications in public security and video surveillance. Recently, leveraging the supervised or semi-unsupervised learning paradigms, which benefits from the large-scale datasets and strong computing performance, has achieved a competitive performance on a specific target domain. However, when Re-ID models are directly deployed in a new domain without target samples, they always suffer from considerable performance degradation and poor domain generalization. To address this challenge, we propose a Deep Multimodal Fusion network to elaborate rich semantic knowledge for assisting in representation learning during the pre-training. Importantly, a multimodal fusion strategy is introduced to translate the features of different modalities into the common space, which can significantly boost generalization capability of Re-ID model. As for the fine-tuning stage, a realistic dataset is adopted to fine-tune the pre-trained model for better distribution alignment with real-world data. Comprehensive experiments on benchmarks demonstrate that our method can significantly outperform previous domain generalization or meta-learning methods with a clear margin. Our source code will also be publicly available at https://github.com/JeremyXSC/DMF.

图像增强旨在通过修饰颜色和音调来提高照片的美学视觉质量，并且是专业数字摄影的必不可少的技术。近年来，基于学习的图像增强算法已达到有希望的表现，并吸引了日益普及。但是，典型的努力试图为所有像素的颜色转换构建一个均匀的增强子。它忽略了对照片重要的不同内容（例如，天空，海洋等）之间的像素差异，从而导致结果不令人满意。在本文中，我们提出了一个新颖的可学习背景知觉的4维查找表（4D LUT），该表通过适应性地学习照片上下文来实现每个图像中不同内容的增强。特别是，我们首先引入一个轻量级上下文编码器和一个参数编码器，以分别学习像素级类别的上下文图和一组图像自适应系数。然后，通过通过系数集成多个基础4D LUT来生成上下文感知的4D LUT。最后，可以通过将源图像和上下文图馈入融合的上下文感知的4D〜LUT来获得增强的图像。与传统的3D LUT（即RGB映射到RGB）相比，通常用于摄像机成像管道系统或工具，4D LUT，即RGBC（RGB+上下文）映射到RGB，可实现具有不同像素的颜色转换的最佳控制每个图像中的内容，即使它们具有相同的RGB值。实验结果表明，我们的方法在广泛使用的基准中优于其他最先进的方法。

人类的姿势估计旨在弄清不同场景中所有人的关键。尽管结果有希望，但目前的方法仍然面临一些挑战。现有的自上而下的方法单独处理一个人，而没有不同的人与所在的场景之间的相互作用。因此，当发生严重闭塞时，人类检测的表现会降低。另一方面，现有的自下而上方法同时考虑所有人，并捕获整个图像的全局知识。但是，由于尺度变化，它们的准确性不如自上而下的方法。为了解决这些问题，我们通过整合自上而下和自下而上的管道来探索不同接受场的视觉线索并实现其互补性，提出了一种新颖的双皮线整合变压器（DPIT）。具体而言，DPIT由两个分支组成，自下而上的分支介绍了整个图像以捕获全局视觉信息，而自上而下的分支则从单人类边界框中提取本地视觉的特征表示。然后，从自下而上和自上而下的分支中提取的特征表示形式被馈入变压器编码器，以交互融合全局和本地知识。此外，我们定义了关键点查询，以探索全景和单人类姿势视觉线索，以实现两个管道的相互互补性。据我们所知，这是将自下而上和自上而下管道与变压器与人类姿势估计的变压器相结合的最早作品之一。关于可可和MPII数据集的广泛实验表明，我们的DPIT与最先进的方法相当。

我们开发了WOC，这是一个基于网络摄像头的3D虚拟在线聊天室，用于多人交互，该聊天介绍了用户的3D运动，并实时驱动其单独的3D虚拟化头像。与现有的基于可穿戴设备的解决方案相比，WOC使用单个相机提供方便和低成本的3D运动捕获。为了促进身临其境的聊天体验，WOC提供了高保真虚拟化的化身操纵，这也支持用户定义的字符。使用分布式数据流服务，系统为所有用户提供高度同步的运动和声音。部署在网站上，无需安装，用户可以在https://yanch.cloud上自由体验虚拟在线聊天。