最近，3D视觉和语言任务吸引了不断增长的研究兴趣。与其他视觉和语言任务相比，3D视觉问题回答（VQA）任务的利用较小，并且更容易受到语言先验和共同参考的歧义。同时，由于规模和注释方法有限，最近提出的几个3D VQA数据集并不能很好地支持3D VQA任务。在这项工作中，我们通过收集一个新的3D VQA数据集（称为FE-3DGQA），正式定义和解决3D接地的VQA任务，并具有多样化且相对自由形式的提问，以及密集和完全接地的边界框注释。为了获得更多可解释的答案，我们标记了出现在复杂的质量检查对中的对象，该对象具有不同的语义类型，包括答案接地的对象（均出现并未出现在问题中），以及用于答案的对象的上下文对象。我们还提出了一个新的3D VQA框架，以有效地预测完全视觉扎根和可解释的答案。广泛的实验证明，我们新收集的基准数据集可有效地用于评估不同方面的各种3D VQA方法，而我们新提出的框架也可以在新的基准数据集中实现最新的性能。新收集的数据集和我们的代码都将在http://github.com/zlccccc/3dgqa上公开获得。

在前景点（即物体）和室外激光雷达点云中的背景点之间通常存在巨大的失衡。它阻碍了尖端的探测器专注于提供信息的区域，以产生准确的3D对象检测结果。本文提出了一个新的对象检测网络，该对象检测网络通过称为PV-RCNN ++的语义点 - 素voxel特征相互作用。与大多数现有方法不同，PV-RCNN ++探索了语义信息，以增强对象检测的质量。首先，提出了一个语义分割模块，以保留更具歧视性的前景关键。这样的模块将指导我们的PV-RCNN ++在关键区域集成了更多与对象相关的点和体素特征。然后，为了使点和体素有效相互作用，我们利用基于曼哈顿距离的体素查询来快速采样关键点周围的体素特征。与球查询相比，这种体素查询将降低从O（N）到O（K）的时间复杂性。此外，为了避免仅学习本地特征，基于注意力的残留点网模块旨在扩展接收场，以将相邻的素素特征适应到关键点中。 Kitti数据集的广泛实验表明，PV-RCNN ++达到81.60 $ \％$，40.18 $ \％$，68.21 $ \％$ \％$ 3D地图在汽车，行人和骑自行车的人方面，可以在州，甚至可以在州立骑行者，甚至更好地绩效-艺术。

Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications in 3D computer vision. The progress of deep learning (DL) has impressively improved the capability and robustness of point cloud completion. However, the quality of completed point clouds is still needed to be further enhanced to meet the practical utilization. Therefore, this work aims to conduct a comprehensive survey on various methods, including point-based, convolution-based, graph-based, and generative model-based approaches, etc. And this survey summarizes the comparisons among these methods to provoke further research insights. Besides, this review sums up the commonly used datasets and illustrates the applications of point cloud completion. Eventually, we also discussed possible research trends in this promptly expanding field.

在本文中，我们解决了预测拥挤空间中的Egentric相机佩戴者（自我）的轨迹的问题。从现实世界中走向周围的不同相机佩戴者数据的数据学到的轨迹预测能力可以转移，以协助导航中的人们在导航中的人们障碍，并在移动机器人中灌输人类导航行为，从而实现更好的人机互动。为此，构建了一个新的Egocentric人类轨迹预测数据集，其中包含在佩戴相机的拥挤空间中导航的人们的真实轨迹，以及提取丰富的上下文数据。我们提取并利用三种不同的方式来预测摄像机佩戴者的轨迹，即他/她过去的轨迹，附近人的过去的轨迹以及场景语义或场景的深度等环境。基于变压器的编码器解码器神经网络模型，与熔化多种方式的新型级联跨关注机构集成，已经设计成预测相机佩戴者的未来轨迹。已经进行了广泛的实验，结果表明，我们的模型在Emocentric人类轨迹预测中优于最先进的方法。

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.