最近，事物的人工智能（Aiot）一直在引起人们的关注，具有通过事物的网络连接提供高度智能服务的有趣愿景，从而导致了先进的AI驱动生态。但是，对数据隐私的最新监管限制排除将敏感的本地数据上传到数据中心，并以集中式方法利用它们。在这种情况下，直接应用联合学习算法几乎不能满足效率和准确性的工业要求。因此，我们在面部识别应用方面为AIOT提出了一个有效的工业联合学习框架。具体而言，我们建议利用转移学习的概念来加快设备上的联合培训，并进一步介绍私人投影仪的新颖设计，该设计有助于保护共享梯度，而不会产生额外的记忆消耗或计算成本。对亚洲私人面部数据集的实证研究表明，我们的方法仅在20轮沟通中就可以实现高认识的准确性，这表明了其在预测和培训方面的有效性。

作为保护隐私的协作机器学习范式，联邦学习在行业中越来越受到关注。随着需求的巨大增长，有许多联合学习平台使联邦参与者可以从头开始建立并建立联合模型。但是，退出的平台高度侵入性，复杂且难以与建造的机器学习模型集成。对于许多已经具有成熟服务模型的现实世界企业，现有的联合学习平台具有很高的进入障碍和发展成本。本文介绍了一个简单而实用的联合学习插件，其灵感来自合奏学习，被称为包装，使参与者能够以最低的成本建立/加入使用现有模型的联合系统。 Wrapperfl通过简单地将其连接到现有模型的输入和输出接口，而无需重新开发，从而大大减少了人力和资源的开销。我们在异质数据分布和异质模型下验证我们的建议方法。实验结果表明，在实际设置下，包装可以成功地应用于广泛的应用程序，并以低成本的联合学习改善本地模型。

由于联邦学习（FL）的分布性质，研究人员发现FL容易受到后门攻击的影响，该攻击旨在将子任务注入FL而不破坏主要任务的性能。当在FL模型收敛上注入时，单发后门攻击在主要任务和后门子任务上都可以达到高度精度。但是，早期注射的单发后门攻击是无效的，因为：（1）由于正常局部更新的稀释效果，在注射时未达到最大的后门效果； （2）后门效应迅速下降，因为后门将被新的普通本地更新所覆盖。在本文中，我们利用FL模型信息泄漏加强了早期注射的单发后门攻击。我们表明，如果客户在模拟整个人群的分布和梯度的数据集上进行训练，则可以加快FL收敛速度。基于这一观察结果，我们提出了两阶段的后门攻击，其中包括随后的后门攻击的初步阶段。在初步阶段，受攻击者控制的客户首先启动了整个人口分布推理攻击，然后在本地制作的数据集上进行训练，该数据集与梯度和推断分布保持一致。从初步阶段中受益，后来注射的后门实现了更好的有效性，因为后门效应不太可能被普通模型更新稀释。在各种数据异质性设置下，在MNIST数据集上进行了广泛的实验，以评估拟议的后门攻击的有效性。结果表明，即使有防御机制，该提议的后门以成功率和寿命都优于现有的后门攻击。

摄影平台已广泛用于摄影测量和机器人感知模块，以稳定相机姿势，从而提高捕获的视频质量。通常，阳性主要由传感器和执行器部件组成。可以将传感器的方向测量直接输入到执行器以转向适当的姿势。但是，现成的定制产品要么非常昂贵，要么取决于高度精确的IMU和带有霍尔传感器的无刷直流电动机以估计角度，这很容易在长期操作中累积漂移。在本文中，提出了一种基于简历的新跟踪和融合算法，专门针对自然界运行的无人机上的gimbal系统，主要贡献如下：部署到Jetson Nano平台上，将图像分为二进制零件（地面和天空）。 b）在3D中以3D为提示跟踪天际线和接地平面的几何原始图，以及IMU的方向估计可以为方向提供多种猜测。 c）基于球形表面的自适应颗粒采样可以有效地从上述传感器来源融合。最终的原型算法在实时嵌入式系统上进行了测试，并且在空气中进行了模拟和实际功能测试。

A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.

In this work, we focus on instance-level open vocabulary segmentation, intending to expand a segmenter for instance-wise novel categories without mask annotations. We investigate a simple yet effective framework with the help of image captions, focusing on exploiting thousands of object nouns in captions to discover instances of novel classes. Rather than adopting pretrained caption models or using massive caption datasets with complex pipelines, we propose an end-to-end solution from two aspects: caption grounding and caption generation. In particular, we devise a joint Caption Grounding and Generation (CGG) framework based on a Mask Transformer baseline. The framework has a novel grounding loss that performs explicit and implicit multi-modal feature alignments. We further design a lightweight caption generation head to allow for additional caption supervision. We find that grounding and generation complement each other, significantly enhancing the segmentation performance for novel categories. We conduct extensive experiments on the COCO dataset with two settings: Open Vocabulary Instance Segmentation (OVIS) and Open Set Panoptic Segmentation (OSPS). The results demonstrate the superiority of our CGG framework over previous OVIS methods, achieving a large improvement of 6.8% mAP on novel classes without extra caption data. Our method also achieves over 15% PQ improvements for novel classes on the OSPS benchmark under various settings.

Nearest-Neighbor (NN) classification has been proven as a simple and effective approach for few-shot learning. The query data can be classified efficiently by finding the nearest support class based on features extracted by pretrained deep models. However, NN-based methods are sensitive to the data distribution and may produce false prediction if the samples in the support set happen to lie around the distribution boundary of different classes. To solve this issue, we present P3DC-Shot, an improved nearest-neighbor based few-shot classification method empowered by prior-driven data calibration. Inspired by the distribution calibration technique which utilizes the distribution or statistics of the base classes to calibrate the data for few-shot tasks, we propose a novel discrete data calibration operation which is more suitable for NN-based few-shot classification. Specifically, we treat the prototypes representing each base class as priors and calibrate each support data based on its similarity to different base prototypes. Then, we perform NN classification using these discretely calibrated support data. Results from extensive experiments on various datasets show our efficient non-learning based method can outperform or at least comparable to SOTA methods which need additional learning steps.

In this tutorial paper, we look into the evolution and prospect of network architecture and propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed architecture has two key elements, i.e., holistic network virtualization and pervasive artificial intelligence (AI). The holistic network virtualization consists of network slicing and digital twin, from the aspects of service provision and service demand, respectively, to incorporate service-centric and user-centric networking. The pervasive network intelligence integrates AI into future networks from the perspectives of networking for AI and AI for networking, respectively. Building on holistic network virtualization and pervasive network intelligence, the proposed architecture can facilitate three types of interplay, i.e., the interplay between digital twin and network slicing paradigms, between model-driven and data-driven methods for network management, and between virtualization and AI, to maximize the flexibility, scalability, adaptivity, and intelligence for 6G networks. We also identify challenges and open issues related to the proposed architecture. By providing our vision, we aim to inspire further discussions and developments on the potential architecture of 6G.