We study a challenging task, conditional human motion generation, which produces plausible human motion sequences according to various conditional inputs, such as action classes or textual descriptors. Since human motions are highly diverse and have a property of quite different distribution from conditional modalities, such as textual descriptors in natural languages, it is hard to learn a probabilistic mapping from the desired conditional modality to the human motion sequences. Besides, the raw motion data from the motion capture system might be redundant in sequences and contain noises; directly modeling the joint distribution over the raw motion sequences and conditional modalities would need a heavy computational overhead and might result in artifacts introduced by the captured noises. To learn a better representation of the various human motion sequences, we first design a powerful Variational AutoEncoder (VAE) and arrive at a representative and low-dimensional latent code for a human motion sequence. Then, instead of using a diffusion model to establish the connections between the raw motion sequences and the conditional inputs, we perform a diffusion process on the motion latent space. Our proposed Motion Latent-based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages. Extensive experiments on various human motion generation tasks demonstrate that our MLD achieves significant improvements over the state-of-the-art methods among extensive human motion generation tasks, with two orders of magnitude faster than previous diffusion models on raw motion sequences.

Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficient and accurate depth estimation models that can run fast on low-power mobile chipsets. In this Mobile AI challenge, the target was to develop deep learning-based single image depth estimation solutions that can show a real-time performance on IoT platforms and smartphones. For this, the participants used a large-scale RGB-to-depth dataset that was collected with the ZED stereo camera capable to generated depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the Raspberry Pi 4 platform, where the developed solutions were able to generate VGA resolution depth maps at up to 27 FPS while achieving high fidelity results. All models developed in the challenge are also compatible with any Android or Linux-based mobile devices, their detailed description is provided in this paper.

在3D视觉中，视觉重新定位已被广泛讨论：鉴于预构建的3D视觉图，估计查询图像的6 DOF（自由度）姿势。大规模室内环境中的重新定位可实现有吸引力的应用程序，例如增强现实和机器人导航。但是，当相机移动时，在这种环境中，外观变化很快，这对于重新定位系统来说是具有挑战性的。为了解决这个问题，我们建议一种基于虚拟视图综合方法Rendernet，以丰富有关此特定情况的数据库和完善姿势。我们选择直接渲染虚拟观点的必要全局和本地特征，而不是渲染需要高质量3D模型的真实图像，并分别将它们应用于后续图像检索和功能匹配操作中。所提出的方法在很大程度上可以改善大规模室内环境中的性能，例如，在INLOC数据集中获得7.1 \％和12.2 \％的改善。

在图像变压器网络的编码器部分中的FineTuning佩带的骨干网一直是语义分段任务的传统方法。然而，这种方法揭示了图像在编码阶段提供的语义上下文。本文认为将图像的语义信息纳入预磨料的基于分层变换器的骨干，而FineTuning可显着提高性能。为实现这一目标，我们提出了一个简单且有效的框架，在语义关注操作的帮助下将语义信息包含在编码器中。此外，我们在训练期间使用轻量级语义解码器，为每个阶段提供监督对中间语义的先前地图。我们的实验表明，结合语义前导者增强了所建立的分层编码器的性能，随着絮凝物的数量略有增加。我们通过将Sromask集成到Swin-Cransformer的每个变体中提供了经验证明，因为我们的编码器与不同的解码器配对。我们的框架在CudeScapes数据集上实现了ADE20K数据集的新型58.22％的MIOU，并在Miou指标中提高了超过3％的内容。代码和检查点在https://github.com/picsart-ai-research/semask-egation上公开使用。

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.

Prior works on Information Extraction (IE) typically predict different tasks and instances (e.g., event triggers, entities, roles, relations) independently, while neglecting their interactions and leading to model inefficiency. In this work, we introduce a joint IE framework, HighIE, that learns and predicts multiple IE tasks by integrating high-order cross-task and cross-instance dependencies. Specifically, we design two categories of high-order factors: homogeneous factors and heterogeneous factors. Then, these factors are utilized to jointly predict labels of all instances. To address the intractability problem of exact high-order inference, we incorporate a high-order neural decoder that is unfolded from a mean-field variational inference method. The experimental results show that our approach achieves consistent improvements on three IE tasks compared with our baseline and prior work.

Recently, human pose estimation mainly focuses on how to design a more effective and better deep network structure as human features extractor, and most designed feature extraction networks only introduce the position of each anatomical keypoint to guide their training process. However, we found that some human anatomical keypoints kept their topology invariance, which can help to localize them more accurately when detecting the keypoints on the feature map. But to the best of our knowledge, there is no literature that has specifically studied it. Thus, in this paper, we present a novel 2D human pose estimation method with explicit anatomical keypoints structure constraints, which introduces the topology constraint term that consisting of the differences between the distance and direction of the keypoint-to-keypoint and their groundtruth in the loss object. More importantly, our proposed model can be plugged in the most existing bottom-up or top-down human pose estimation methods and improve their performance. The extensive experiments on the benchmark dataset: COCO keypoint dataset, show that our methods perform favorably against the most existing bottom-up and top-down human pose estimation methods, especially for Lite-HRNet, when our model is plugged into it, its AP scores separately raise by 2.9\% and 3.3\% on COCO val2017 and test-dev2017 datasets.

Document images are a ubiquitous source of data where the text is organized in a complex hierarchical structure ranging from fine granularity (e.g., words), medium granularity (e.g., regions such as paragraphs or figures), to coarse granularity (e.g., the whole page). The spatial hierarchical relationships between content at different levels of granularity are crucial for document image understanding tasks. Existing methods learn features from either word-level or region-level but fail to consider both simultaneously. Word-level models are restricted by the fact that they originate from pure-text language models, which only encode the word-level context. In contrast, region-level models attempt to encode regions corresponding to paragraphs or text blocks into a single embedding, but they perform worse with additional word-level features. To deal with these issues, we propose MGDoc, a new multi-modal multi-granular pre-training framework that encodes page-level, region-level, and word-level information at the same time. MGDoc uses a unified text-visual encoder to obtain multi-modal features across different granularities, which makes it possible to project the multi-granular features into the same hyperspace. To model the region-word correlation, we design a cross-granular attention mechanism and specific pre-training tasks for our model to reinforce the model of learning the hierarchy between regions and words. Experiments demonstrate that our proposed model can learn better features that perform well across granularities and lead to improvements in downstream tasks.

语义Web技术已成功促进了许多具有丰富数据表示方法的RDF模型。它还具有代表和存储多模式知识库（例如多模式场景图）的潜在能力。但是，大多数现有的查询语言，尤其是SPARQL，几乎没有探索隐式多模式关系，例如语义相似性，空间关系等。我们首先通过在RDF图数据库中组织一个大型场景图（即视觉基因组）来探索此问题。基于建议的RDF存储的多模式场景图，我们扩展了SPARQL查询，以回答包含有关颜色，空间等关系推理的问题。进一步的演示（即VGStore）显示了定制查询和显示多模式数据的有效性。

Twitter机器人检测是一项重要且有意义的任务。现有的基于文本的方法可以深入分析用户推文内容，从而实现高性能。但是，新颖的Twitter机器人通过窃取真正的用户的推文并用良性推文稀释恶意内容来逃避这些检测。这些新颖的机器人被认为以语义不一致的特征。此外，最近出现了利用Twitter图结构的方法，显示出巨大的竞争力。但是，几乎没有一种方法使文本和图形模式深入融合并进行了交互，以利用优势并了解两种方式的相对重要性。在本文中，我们提出了一个名为BIC的新型模型，该模型使文本和图形模式深入互动并检测到推文语义不一致。具体而言，BIC包含一个文本传播模块，一个图形传播模块，可分别在文本和图形结构上进行机器人检测，以及可证明有效的文本互动模块，以使两者相互作用。此外，BIC还包含一个语义一致性检测模块，以从推文中学习语义一致性信息。广泛的实验表明，我们的框架在全面的Twitter机器人基准上优于竞争基准。我们还证明了拟议的相互作用和语义一致性检测的有效性。