Given a piece of text, a video clip and a reference audio, the movie dubbing (also known as visual voice clone V2C) task aims to generate speeches that match the speaker's emotion presented in the video using the desired speaker voice as reference. V2C is more challenging than conventional text-to-speech tasks as it additionally requires the generated speech to exactly match the varying emotions and speaking speed presented in the video. Unlike previous works, we propose a novel movie dubbing architecture to tackle these problems via hierarchical prosody modelling, which bridges the visual information to corresponding speech prosody from three aspects: lip, face, and scene. Specifically, we align lip movement to the speech duration, and convey facial expression to speech energy and pitch via attention mechanism based on valence and arousal representations inspired by recent psychology findings. Moreover, we design an emotion booster to capture the atmosphere from global video scenes. All these embeddings together are used to generate mel-spectrogram and then convert to speech waves via existing vocoder. Extensive experimental results on the Chem and V2C benchmark datasets demonstrate the favorable performance of the proposed method. The source code and trained models will be released to the public.

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.

我们提出MAPTR，这是一个结构化的端到端框架，用于有效的在线矢量化高清图构建。我们提出了一种基于统一的建模方法，即将MAP元素建模为具有一组等效排列的点集，从而避免了地图元素的定义歧义并简化学习。我们采用层次查询嵌入方案来灵活编码结构化的地图信息，并对地图元素学习执行层次结构匹配。 MAPTR在Nuscenes数据集上实现了现有的矢量化MAP构造方法的最佳性能和效率。尤其是，MAPTR-NANO以RTX 3090的实时推理速度（$ 25.1 $ fps）运行，比现有的基于最新的摄像头方法快$ 8 \ times $ $，同时获得$ 3.3 $较高的地图。 Maptr-tiny在更快的速度的同时显着优于现有的最新多模式方法$ 13.5 $地图。定性结果表明，MAPTR在复杂和各种驾驶场景中保持稳定且强大的地图构造质量。可在\ url {https://github.com/hustvl/maptr}上获得丰富的演示，以证明在现实世界情景中的有效性。 MAPTR在自动驾驶中具有巨大的应用价值。代码将发布以促进进一步的研究和应用。

我们在ISWC 2022上对知识图模型的知识形象人群提出了一个用于语言模型的系统，该系统对知识库构建（LM-KBC）挑战进行了评估。我们的系统涉及特定于任务的预培训以改善蒙版的LM表示。对象令牌，促使分解候选对象以及其他高质量检索的方法。我们的系统是基于BERT LM的LM-KBC挑战赛曲目1的获胜者；它在挑战的隐藏测试集中获得了55.0％的F-1得分。

基于环绕视图摄像机系统的3D检测是自动驾驶中的一项关键技术。在这项工作中，我们提出了3D检测的极性参数化，该参数化重新定义了偏振系统中的位置参数化，速度分解，感知范围，标签分配和损失函数。极性参数化建立了图像模式与预测目标之间的明确关联，从而利用环绕视觉摄像机的视图对称性为感应偏置，以减轻优化和增强性能。基于极性参数化，我们提出了一个名为polardetr的环绕视图3D检测变压器。Polardetr在不同的主链配置上实现了有希望的性能速度权衡。此外，在提交时间（2022年3月4日）的3D检测和3D跟踪方面，Polardetr在Nuscenes基准的排行榜上排名第一。代码将以\ url {https://github.com/hustvl/polardetr}发布。

Contextual information is vital in visual understanding problems, such as semantic segmentation and object detection. We propose a Criss-Cross Network (CCNet) for obtaining full-image contextual information in a very effective and efficient way. Concretely, for each pixel, a novel criss-cross attention module harvests the contextual information of all the pixels on its criss-cross path. By taking a further recurrent operation, each pixel can finally capture the full-image dependencies. Besides, a category consistent loss is proposed to enforce the criss-cross attention module to produce more discriminative features. Overall, CCNet is with the following merits: 1) GPU memory friendly. Compared with the non-local block, the proposed recurrent criss-cross attention module requires 11× less GPU memory usage. 2) High computational efficiency. The recurrent criss-cross attention significantly reduces FLOPs by about 85% of the non-local block. 3) The state-of-the-art performance. We conduct extensive experiments on semantic segmentation benchmarks including Cityscapes, ADE20K, human parsing benchmark LIP, instance segmentation benchmark COCO, video segmentation benchmark CamVid. In particular, our CCNet achieves the mIoU scores of 81.9%, 45.76% and 55.47% on the Cityscapes test set, the ADE20K validation set and the LIP validation set respectively, which are the new state-of-the-art results. The source codes are available at https://github.com/speedinghzl/CCNet.

Passive monitoring of acoustic or radio sources has important applications in modern convenience, public safety, and surveillance. A key task in passive monitoring is multiobject tracking (MOT). This paper presents a Bayesian method for multisensor MOT for challenging tracking problems where the object states are high-dimensional, and the measurements follow a nonlinear model. Our method is developed in the framework of factor graphs and the sum-product algorithm (SPA). The multimodal probability density functions (pdfs) provided by the SPA are effectively represented by a Gaussian mixture model (GMM). To perform the operations of the SPA in high-dimensional spaces, we make use of Particle flow (PFL). Here, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. This makes it possible to obtain good object detection and tracking performance even in challenging multisensor MOT scenarios with single sensor measurements that have a lower dimension than the object positions. We perform a numerical evaluation in a passive acoustic monitoring scenario where multiple sources are tracked in 3-D from 1-D time-difference-of-arrival (TDOA) measurements provided by pairs of hydrophones. Our numerical results demonstrate favorable detection and estimation accuracy compared to state-of-the-art reference techniques.

Location-aware networks will introduce new services and applications for modern convenience, surveillance, and public safety. In this paper, we consider the problem of cooperative localization in a wireless network where the position of certain anchor nodes can be controlled. We introduce an active planning method that aims at moving the anchors such that the information gain of future measurements is maximized. In the control layer of the proposed method, control inputs are calculated by minimizing the traces of approximate inverse Bayesian Fisher information matrixes (FIMs). The estimation layer computes estimates of the agent states and provides Gaussian representations of marginal posteriors of agent positions to the control layer for approximate Bayesian FIM computations. Based on a cost function that accumulates Bayesian FIM contributions over a sliding window of discrete future timesteps, a receding horizon (RH) control is performed. Approximations that make it possible to solve the resulting tree-search problem efficiently are also discussed. A numerical case study demonstrates the intelligent behavior of a single controlled anchor in a 3-D scenario and the resulting significantly improved localization accuracy.

Source-free domain adaptation aims to adapt a source model trained on fully-labeled source domain data to a target domain with unlabeled target domain data. Source data is assumed inaccessible due to proprietary or privacy reasons. Existing works use the source model to pseudolabel target data, but the pseudolabels are unreliable due to data distribution shift between source and target domain. In this work, we propose to leverage an ImageNet pre-trained feature extractor in a new co-learning framework to improve target pseudolabel quality for finetuning the source model. Benefits of the ImageNet feature extractor include that it is not source-biased and it provides an alternate view of features and classification decisions different from the source model. Such pre-trained feature extractors are also publicly available, which allows us to readily leverage modern network architectures that have strong representation learning ability. After co-learning, we sharpen predictions of non-pseudolabeled samples by entropy minimization. Evaluation on 3 benchmark datasets show that our proposed method can outperform existing source-free domain adaptation methods, as well as unsupervised domain adaptation methods which assume joint access to source and target data.

As a neural network compression technique, post-training quantization (PTQ) transforms a pre-trained model into a quantized model using a lower-precision data type. However, the prediction accuracy will decrease because of the quantization noise, especially in extremely low-bit settings. How to determine the appropriate quantization parameters (e.g., scaling factors and rounding of weights) is the main problem facing now. Many existing methods determine the quantization parameters by minimizing the distance between features before and after quantization. Using this distance as the metric to optimize the quantization parameters only considers local information. We analyze the problem of minimizing local metrics and indicate that it would not result in optimal quantization parameters. Furthermore, the quantized model suffers from overfitting due to the small number of calibration samples in PTQ. In this paper, we propose PD-Quant to solve the problems. PD-Quant uses the information of differences between network prediction before and after quantization to determine the quantization parameters. To mitigate the overfitting problem, PD-Quant adjusts the distribution of activations in PTQ. Experiments show that PD-Quant leads to better quantization parameters and improves the prediction accuracy of quantized models, especially in low-bit settings. For example, PD-Quant pushes the accuracy of ResNet-18 up to 53.08% and RegNetX-600MF up to 40.92% in weight 2-bit activation 2-bit. The code will be released at https://github.com/hustvl/PD-Quant.