使用神经网络编码HyperGraphs的HyperGraph神经网络（HNNS）为建模数据中的高阶关系提供了一种有希望的方法，并进一步解决了基于此类高阶关系的相关预测任务。但是，实践中的高阶关系包含复杂的模式，通常是高度不规则的。因此，设计一个足以表达这些关系的HNN在保持计算效率的同时，通常是一项挑战。受到超图扩散算法的启发，这项工作提出了一种名为ED-HNN的新型HNN体系结构，该结构可证明可以代表任何可以建模广泛的高阶关系的连续均值超差扩散算子。 ED-HNN可以通过将超图的星形扩展与传递神经网络的标准消息相结合来有效地实现。 ED-HNN进一步在处理异性超图和建造深层模型方面表现出了极大的优势。我们评估了在9个现实世界中的HyperGraph数据集上进行节点分类的ED-HNN。 ED-HNN均匀地胜过这9个数据集的最佳基线，并在其中四个数据集中获得了超过2 \％$ \ uparrow $的预测准确性。

Incorporating knowledge graph as side information has become a new trend in recommendation systems. Recent studies regard items as entities of a knowledge graph and leverage graph neural networks to assist item encoding, yet by considering each relation type individually. However, relation types are often too many and sometimes one relation type involves too few entities. We argue that it is not efficient nor effective to use every relation type for item encoding. In this paper, we propose a VRKG4Rec model (Virtual Relational Knowledge Graphs for Recommendation), which explicitly distinguish the influence of different relations for item representation learning. We first construct virtual relational graphs (VRKGs) by an unsupervised learning scheme. We also design a local weighted smoothing (LWS) mechanism for encoding nodes, which iteratively updates a node embedding only depending on the embedding of its own and its neighbors, but involve no additional training parameters. We also employ the LWS mechanism on a user-item bipartite graph for user representation learning, which utilizes encodings of items with relational knowledge to help training representations of users. Experiment results on two public datasets validate that our VRKG4Rec model outperforms the state-of-the-art methods. The implementations are available at https://github.com/lulu0913/VRKG4Rec.

昂贵的边界盒注释限制了对象检测任务的开发。因此，有必要专注于更具挑战性的对象检测的更具挑战性的任务。它要求检测器只有几个训练样本识别新型类别的对象。如今，许多采用类似于元学习的培训方式的现有流行方法已经达到了有希望的表现，例如meta r-CNN系列。但是，支持数据仅用作类的注意，以指导每次查询图像的检测。它们彼此的相关性仍未得到解释。此外，许多最近的作品将支持数据和查询图像视为独立分支，而无需考虑它们之间的关系。为了解决这个问题，我们提出了一个动态相关性学习模型，该模型利用查询图像上所有支持图像与目标区域（ROI）之间的关系来构建动态图卷积网络（GCN）。通过使用此GCN的输出调整基本检测器的预测分布，提出的模型是一项硬辅助分类任务，该任务指导检测器隐含地改进类表示。对Pascal VOC和MS-Coco数据集进行了全面的实验。拟议的模型达到了最佳的整体性能，这表明了其学习更多广义特征的有效性。我们的代码可在https://github.com/liuweijie19980216/drl-for-fsod上找到。

以自我为中心的视频为人类行为的高保真建模提供了细粒度的信息。手和互动对象是理解观众的行为和意图的一个关键方面。我们提供了一个标记的数据集，该数据集由11,243张以egentric的图像组成，并在各种日常活动中与手动和物体相互作用的每个像素分割标签。我们的数据集是第一个标记详细的手动触点边界的数据集。我们介绍了一种上下文感知的组成数据增强技术，以适应YouTube Eginbecentric视频的分布。我们表明，我们的强大手动分割模型和数据集可以作为基础工具，以提高或启用几个下游视觉应用程序，包括手状态分类，视频活动识别，3D网格对手相互作用的3D网格重建以及视频的视频介绍。 - 以自我为中心的视频中的对象前景。数据集和代码可在以下网址找到：https：//github.com/owenzlz/egohos

Graph-based learning is a rapidly growing sub-field of machine learning with applications in social networks, citation networks, and bioinformatics. One of the most popular models is graph attention networks. They were introduced to allow a node to aggregate information from features of neighbor nodes in a non-uniform way, in contrast to simple graph convolution which does not distinguish the neighbors of a node. In this paper, we study theoretically this expected behaviour of graph attention networks. We prove multiple results on the performance of graph attention mechanism for the problem of node classification for a contextual stochastic block model. Here the node features are obtained from a mixture of Gaussians and the edges from a stochastic block model. We show that in an "easy" regime, where the distance between the means of the Gaussians is large enough, graph attention is able to distinguish inter-class from intra-class edges, and thus it maintains the weights of important edges and significantly reduces the weights of unimportant edges. Consequently, we show that this implies perfect node classification. In the "hard" regime, we show that every attention mechanism fails to distinguish intra-class from inter-class edges. We evaluate our theoretical results on synthetic and real-world data.

多臂匪徒（MAB）提供了一种原则性的在线学习方法，以达到探索和剥削之间的平衡。由于表现出色和反馈学习低，没有学习在多种情况下采取行动，因此多臂匪徒在诸如推荐系统等应用程序中引起了广泛的关注。同样，在推荐系统中，协作过滤（CF）可以说是推荐系统中最早，最具影响力的方法。至关重要的是，新用户和不断变化的推荐项目池是推荐系统需要解决的挑战。对于协作过滤，经典方法是训练模型离线，然后执行在线测试，但是这种方法无法再处理用户偏好的动态变化，即所谓的冷启动。那么，如何在没有有效信息的情况下有效地向用户推荐项目？为了解决上述问题，已经提出了一个基于多臂强盗的协作过滤推荐系统，名为BanditMF。 BANDITMF旨在解决多军强盗算法和协作过滤中的两个挑战：（1）如何在有效信息稀缺的条件下解决冷启动问题以进行协作过滤，（2）强大社会关系域中的强盗算法问题是由独立估计与每个用户相关的未知参数并忽略用户之间的相关性引起的。

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.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

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.

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.