由于难以收集配对的现实世界训练数据，因此图像deraining目前由监督学习主导，并通过Photoshop渲染生成的合成数据。但是，由于合成数据和现实世界数据之间的差距，通常限制了对真实下雨场景的概括。在本文中，我们首先从统计学上探讨了为什么监督模型不能很好地推广到真实的雨天，并找到合成和真实雨水数据的实质差异。受我们的研究的启发，我们建议通过从其他连接的任务中学习有利的代表来消除雨水。在连接的任务中，可以轻松获得真实数据的标签。因此，我们的核心思想是通过任务传输从真实数据中学习表示形式，以改善概括。因此，我们将学习策略称为\ textit {任务传输学习}。如果有多个连接的任务，我们建议通过知识蒸馏降低模型大小。连接任务的预处理模型被视为教师，他们的所有知识都被蒸馏到学生网络，以便我们减少模型规模，同时保留所有连接的任务中有效的先前表示。最后，学生网络对少数配对的合成雨数据进行了微调，以指导预定的先前表示以去除雨水。广泛的实验表明，提出的任务转移学习策略令人惊讶地成功，并与最先进的监督学习方法相比，并显然超过了其他半监督者在合成数据上的方法。特别是，它显示出对现实世界的概括性的概括。

高动态范围（HDR）DEGHOSTING算法旨在生成具有现实细节的无幽灵HDR图像。受到接收场的局部性的限制，现有的基于CNN的方法通常容易产生大型运动和严重饱和的情况下产生鬼影和强度扭曲。在本文中，我们提出了一种新颖的背景感知视觉变压器（CA-VIT），用于无幽灵的高动态范围成像。 CA-VIT被设计为双分支结构，可以共同捕获全球和本地依赖性。具体而言，全球分支采用基于窗口的变压器编码器来建模远程对象运动和强度变化以解决hosting。对于本地分支，我们设计了局部上下文提取器（LCE）来捕获短范围的图像特征，并使用频道注意机制在提取的功能上选择信息丰富的本地详细信息，以补充全局分支。通过将CA-VIT作为基本组件纳入基本组件，我们进一步构建了HDR-Transformer，这是一个分层网络，以重建高质量的无幽灵HDR图像。在三个基准数据集上进行的广泛实验表明，我们的方法在定性和定量上优于最先进的方法，而计算预算大大降低。代码可从https://github.com/megvii-research/hdr-transformer获得

机器人操纵的最新工作集中在遮挡下混乱空间中的物体检索。然而，大多数努力都缺乏对方法完整性的条件分析，或者仅在可以从工作空间中删除对象时，这些方法仅适用。这项工作制定了一般的，闭塞感知的操纵任务，并专注于在限制空间内与现场重排的安全对象重建。它提出了一个框架，可确保安全性保证。此外，通过与在模拟中随机生成的实验中的随机和贪婪的基线进行比较，从经验上开发和评估了这种单调实例的抽象框架的实例化。即使对于具有逼真物体的混乱场景，提议的算法也显着超过基准，并在实验条件下保持高成功率。

在本文中，我们提供了一种使用图形神经网络（GNNS）的理论，用于多节点表示学习（我们有兴趣学习一组多个节点的表示）。我们知道GNN旨在学习单节点表示。当我们想学习涉及多个节点的节点集表示时，先前作品中的常见做法是直接将GNN学习的多节点表示与节点集的关节表示。在本文中，我们显示了这种方法的基本限制，即无法捕获节点集中节点之间的依赖性，并且认为直接聚合各个节点表示不会导致多个节点的有效关节表示。然后，我们注意到，以前的一些成功的工作作品用于多节点表示学习，包括密封，距离编码和ID-GNN，所有使用的节点标记。这些方法根据应用GNN之前的与目标节点集的关系，首先标记图中的节点。然后，在标记的图表中获得的节点表示被聚合到节点集表示中。通过调查其内部机制，我们将这些节点标记技术统一到单个和最基本的形式，即标记技巧。我们证明，通过标记技巧，可以获得足够富有表现力的GNN学习最具表现力的节点集表示，因此原则上可以解决节点集的任何联合学习任务。关于一个重要的双节点表示学习任务，链接预测，验证了我们理论的实验。我们的工作建立了使用GNN在节点集上使用GNN进行联合预测任务的理论基础。

动态图形表示学习是具有广泛应用程序的重要任务。以前关于动态图形学习的方法通常对嘈杂的图形信息（如缺失或虚假连接）敏感，可以产生退化的性能和泛化。为了克服这一挑战，我们提出了一种基于变换器的动态图表学习方法，命名为动态图形变换器（DGT），带有空间 - 时间编码，以有效地学习图形拓扑并捕获隐式链接。为了提高泛化能力，我们介绍了两个补充自我监督的预训练任务，并表明共同优化了两种预训练任务，通过信息理论分析导致较小的贝叶斯错误率。我们还提出了一个时间联盟图形结构和目标 - 上下文节点采样策略，用于高效和可扩展的培训。与现实世界数据集的广泛实验说明了与几个最先进的基线相比，DGT呈现出优异的性能。

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.