知识图（KGS）代表作为三元组的事实已被广泛采用在许多应用中。 LIGHT预测和规则感应等推理任务对于KG的开发很重要。已经提出了知识图形嵌入式（KGES）将kg的实体和kg与持续向量空间的关系进行了建议，以获得这些推理任务，并被证明是有效和强大的。但在实际应用中申请和部署KGE的合理性和可行性尚未探索。在本文中，我们讨论并报告我们在真实域应用程序中部署KGE的经验：电子商务。我们首先为电子商务KG系统提供三个重要的探索者：1）注意推理，推理几个目标关系更为关注而不是全部; 2）解释，提供预测的解释，帮助用户和业务运营商理解为什么预测; 3）可转让规则，生成可重用的规则，以加速将千克部署到新系统。虽然非现有KGE可以满足所有这些DesiderATA，但我们提出了一种新颖的一种，可说明的知识图表注意网络，通过建模三元组之间的相关性而不是纯粹依赖于其头实体，关系和尾部实体嵌入来预测。它可以自动选择预测的注意力三倍，并同时记录它们的贡献，从该解释可以很容易地提供，可以有效地生产可转移规则。我们经验表明，我们的方法能够在我们的电子商务应用程序中满足所有三个DesiderATA，并从实际域应用程序中倾斜于数据集的典型基线。

We propose a new neural network design paradigm Reversible Column Network (RevCol). The main body of RevCol is composed of multiple copies of subnetworks, named columns respectively, between which multi-level reversible connections are employed. Such architectural scheme attributes RevCol very different behavior from conventional networks: during forward propagation, features in RevCol are learned to be gradually disentangled when passing through each column, whose total information is maintained rather than compressed or discarded as other network does. Our experiments suggest that CNN-style RevCol models can achieve very competitive performances on multiple computer vision tasks such as image classification, object detection and semantic segmentation, especially with large parameter budget and large dataset. For example, after ImageNet-22K pre-training, RevCol-XL obtains 88.2% ImageNet-1K accuracy. Given more pre-training data, our largest model RevCol-H reaches 90.0% on ImageNet-1K, 63.8% APbox on COCO detection minival set, 61.0% mIoU on ADE20k segmentation. To our knowledge, it is the best COCO detection and ADE20k segmentation result among pure (static) CNN models. Moreover, as a general macro architecture fashion, RevCol can also be introduced into transformers or other neural networks, which is demonstrated to improve the performances in both computer vision and NLP tasks. We release code and models at https://github.com/megvii-research/RevCol

最近，蒙面图像建模（MIM）在自我监视的视觉识别方面取得了巨大的成功。但是，作为一个基于重建的框架，了解MIM的工作原理仍然是一个悬而未决的问题，因为MIM与以前研究过的暹罗方法（例如对比度学习）有很大不同。在本文中，我们提出了一个新的观点：MIM隐含地学习咬合不变特征，这与其他暹罗方法类似，而后者则学习其他不变性。通过将MIM公式放松为等效的暹罗形式，可以用常规方法在统一框架中解释MIM方法，其中只有a）数据转换，即学习什么不变性，b）相似性测量是不同的。此外，以Mae（He等）为MIM的一个代表性示例，我们从经验上发现MIM模型的成功与选择相似性功能的选择有点联系，但是蒙面图像引入了学习的咬合不变特征 - 事实证明对于视觉变压器来说，这是一个受欢迎的初始化，即使学习的功能可能不太语义。我们希望我们的发现能够激发研究人员在计算机视觉社区中开发更强大的自我监督方法。

我们专注于更好地理解增强不变代表性学习的关键因素。我们重新访问moco v2和byol，并试图证明以下假设的真实性：不同的框架即使具有相同的借口任务也会带来不同特征的表示。我们建立了MoCo V2和BYOL之间公平比较的第一个基准，并观察：（i）复杂的模型配置使得可以更好地适应预训练数据集； （ii）从实现竞争性转移表演中获得的预训练和微调阻碍模型的优化策略不匹配。鉴于公平的基准，我们进行进一步的研究并发现网络结构的不对称性赋予对比框架在线性评估协议下正常工作，同时可能会损害长尾分类任务的转移性能。此外，负样本并不能使模型更明智地选择数据增强，也不会使不对称网络结构结构。我们相信我们的发现为将来的工作提供了有用的信息。

很少有语义细分旨在识别一个看不见类别的对象区域，只有几个带注释的示例作为监督。几次分割的关键是在支持图像和查询图像之间建立牢固的语义关系，并防止过度拟合。在本文中，我们提出了一个有效的多相似性超关联网络（MSHNET），以解决几个射击语义分割问题。在MSHNET中，我们提出了一种新的生成原型相似性（GPS），与余弦相似性可以在支持图像和查询图像之间建立牢固的语义关系。基于全局特征的本地生成的原型相似性在逻辑上与基于本地特征的全局余弦相似性互补，并且可以通过同时使用两个相似性来更全面地表达查询图像和受支持图像之间的关系。此外，我们提出了MSHNET中的对称合并块（SMB），以有效合并多层，多弹射和多相似性超相关特征。 MSHNET是基于相似性而不是特定类别特征而构建的，这些特征可以实现更一般的统一性并有效地减少过度拟合。在两个基准的语义分割数据集Pascal-5i和Coco-20i上，MSHNET在1次和5次语义分段任务上实现了新的最先进的表演。

机器学习模型被证明是面对模型提取攻击的严重威胁，其中服务提供商拥有的训练有素的私人模型可以被假装作为客户端的攻击者窃取。不幸的是，先前的作品侧重于欧几里德空间训练的模型，例如图像和文本，而如何提取包含图形结构的GNN模型，则尚未探索节点功能。本文首次全面调查并开发针对GNN模型的模型提取攻击。我们首先通过考虑由攻击者获得的节点的不同背景知识，将对冲威胁分类为七种类别的威胁建模并将对抗性威胁分类为七个类别。然后我们展示了利用每种威胁中的可访问知识来实现​​攻击的详细方法。通过评估三个现实世界数据集，我们的攻击显示有效提取重复模型，即目标域中的84％ - 89％的输入具有与受害者模型相同的输出预测。

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.