Point Cloud上采样是增强现实，虚拟现实和触觉场景所必需的。尽管对几何形状的提升进行了充分的研究以使点云坐标致密，但颜色的上采样已在很大程度上被忽略了。在本文中，我们提出了Cu-net，这是第一个深度学习点云颜色上采样模型。基于稀疏卷积和基于神经隐式函数的颜色预测模块利用特征提取器，Cu-net实现了线性时间和空间的复杂性。因此，在理论上，CU-NET比具有二次复杂性的大多数现有方法更有效。实验结果表明，Cu-net可以实时用近一百万分为单位逼真的点云上色，同时具有比基线更好的视觉质量。此外，Cu-net可以适应任意的上采样比和看不见的对象。我们的源代码将很快发布给公众。

无监督的视频域适应是一项实用但具有挑战性的任务。在这项工作中，我们第一次从脱离视图中解决了它。我们的关键想法是在适应过程中将与域相关的信息从数据中删除。具体而言，我们考虑从两组潜在因素中生成跨域视频，一个编码静态域相关信息，另一个编码时间和语义相关的信息。然后开发转移顺序的VAE（Transvae）框架以建模这种产生。为了更好地适应适应，我们进一步提出了几个目标，以限制Transvae中的潜在因素。与几种最先进的方法相比，对UCF-HMDB，小丑和Epic-Kitchens数据集进行了广泛的实验验证了Transvae的有效性和优势。代码可在https://github.com/ldkong1205/transvae上公开获取。

密集的注释LiDAR点云是昂贵的，这限制了完全监督学习方法的可伸缩性。在这项工作中，我们研究了激光雷达分割中未充满激光的半监督学习（SSL）。我们的核心思想是利用激光点云的强烈空间提示来更好地利用未标记的数据。我们建议Lasermix混合不同激光扫描的激光束，然后鼓励模型在混合前后进行一致和自信的预测。我们的框架具有三个吸引人的属性：1）通用：Lasermix对LIDAR表示不可知（例如，范围视图和体素），因此可以普遍应用我们的SSL框架。 2）从统计上讲：我们提供详细的分析，以理论上解释所提出的框架的适用性。 3）有效：对流行激光雷达分割数据集（Nuscenes，Semantickitti和Scribblekitti）的全面实验分析证明了我们的有效性和优势。值得注意的是，我们在标签少2倍至5倍的同行中获得了竞争成果，并平均将仅监督的基线提高了10.8％。我们希望这个简洁而高性能的框架可以促进半监督的激光雷达细分的未来研究。代码将公开可用。

将从标记的源域中学习的知识传输到未经监督域适应的原始目标域（UDA）对于自主驱动系统的可扩展部署至关重要。 UDA中最先进的方法经常采用关键概念：利用来自源域（带地理）的联合监督信号和目标域（带伪标签）进行自培训。在这项工作中，我们在这方面改进并延伸。我们介绍了Conda，一种基于连接的域改性框架，用于LIDAR语义分割，：（1）构建由来自源极和目标域的细粒度交换信号组成的中间域，而不会破坏自我周围物体和背景的语义一致性。车辆; （2）利用中级领域进行自我培训。此外，为了改善源域的网络培训和中间域的自我训练，我们提出了一种抗锯齿规范器和熵聚合器，以减少混叠伪影和嘈杂的目标预测的不利影响。通过广泛的实验，我们证明，与现有技术相比，公园在减轻域间隙方面明显更有效。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.