通过利用预熟gan的潜在空间，已经提出了许多最近的作品来进行面部图像编辑。但是，很少有尝试将它们直接应用于视频，因为1）他们不能保证时间一致性，2）他们的应用受到视频的处理速度的限制，3）他们无法准确编码面部运动和表达的细节。为此，我们提出了一个新颖的网络，将面部视频编码到Stylegan的潜在空间中，以进行语义面部视频操纵。基于视觉变压器，我们的网络重复了潜在向量的高分辨率部分，以实现时间一致性。为了捕捉微妙的面部运动和表情，我们设计了涉及稀疏面部地标和密集的3D脸部网眼的新颖损失。我们已经彻底评估了我们的方法，并成功证明了其对各种面部视频操作的应用。特别是，我们提出了一个新型网络，用于3D坐标系中的姿势/表达控制。定性和定量结果都表明，我们的方法可以显着优于现有的单图方法，同时实现实时（66 fps）速度。

实时视频广播通常需要具有域知识的多种技能和专业知识，以实现多摄像头制作。随着摄像机的数量不断增加，指导现场运动广播现在比以往任何时候都变得更加复杂和挑战。在生产过程中，广播董事需要更加集中，响应，令人满意的知识。为了使董事免于其密集努力，我们开发了一个叫做智能总监的创新自动化体育广播指示系统，旨在模仿典型的人类循环广播过程，以实时自动创建近专业广播节目通过使用一组高级多视图视频分析算法。灵感来自于所谓的“三事”的体育广播建设，我们用一个由三个连续新型组件组成的事件驱动管道构建我们的系统：1）通过建模多视图相关性来检测事件的多视图事件定位2）多视图突出显示检测通过视图选择的视觉重视等级相机视图，3）自动广播调度程序来控制广播视频的生产。为了我们的最佳知识，我们的系统是用于多摄像机运动广播的第一个端到端的自动化指导系统，完全受到体育赛事的语义理解。它还是通过跨视网膜关系建模解决多视图联合事件检测的新问题的第一系统。我们对现实世界的多相机足球数据集进行客观和主观评估，这证明了我们的自动生成视频的质量与人类导向的质量相当。由于其更快的回应，我们的系统能够捕获更快速的快速和短期持续时间，通常由人道持有。

由于其高实用价值，无监督的域自适应人员重新识别受到显着的关注。在过去几年中，通过遵循聚类和FineTuning范式，研究人员建议利用他们的师生框架，以减少不同人重新识别数据集之间的域间差距。受到最近的教师学生框架基于方法的启发，它试图通过使学生从教师直接复制行为来模仿人类学习过程，或者选择可靠的学习材料，我们建议进行进一步的探索，以模仿不同方面的人类学习过程，\ Texit {IE}，自适应更新学习材料，选择性地模仿教师行为，分析学习材料结构。探索的三个组件共同合作，构成了一个新的无监督域自适应人重新识别的方法，称为人类学习仿框架。三个基准数据集的实验结果证明了我们提出的方法的功效。

用于检测CT肺血管造影（CTPA）图像上的肺栓塞（PES）的自动化方法是高需求。现有方法通常采用单独的步骤进行PE候选检测和假阳性去除，而不考虑另一步骤的能力。结果，大多数现有方法通常遭受高误率，以达到可接受的敏感性。本研究介绍了一个端到端的培训卷积神经网络（CNN），其中两个步骤共同优化。所提出的CNN由三个连接子网组成：1）一种用于检测包含可疑PES的多维数据集的新型3D候选提案网络，2）用于生成用于候选的固定血管对齐图像表示的3D空间转换子网，以及3）2D分类网络将转换立方体的三个横截面作为输入，消除了误报。我们使用PE挑战的20个CTPA测试数据集评估了我们的方法，在0mm，2mm和5mm定位误差下，实现了78.9％，80.7％和80.7％的灵敏度，2mm和5mm定位误差，其优于状态 - 最新方法。我们进一步在我们自己的数据集中进一步评估了我们的系统，该数据集由129个CTPA数据组成，共269个栓子。我们的系统在0mm，2mm和5mm定位误差下每卷的2个假阳性达到63.2％，78.9％和86.8％的灵敏度。

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.

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.