由于异质访问点（APS）的性质，负载平衡（LB）是混合灯保真度（LIFI）和无线保真度（WIFI）网络（HLWNETS）的挑战性问题。机器学习有可能以近乎最佳的网络性能为培训过程提供复杂性的LB解决方案。但是，当网络环境（尤其是用户数量）更改时，需要进行最先进的（SOTA）学习辅助LB方法，这大大限制了其实用性。在本文中，提出了一个新颖的深神经网络（DNN）结构，称为自适应目标条件神经网络（A-TCNN），该结构在其他用户的条件下为一个目标用户进行AP选择。此外，开发了一种自适应机制，可以通过分配数据速率要求将较大数量的用户映射到较大的数字，而不会影响目标用户的AP选择结果。这使提出的方法可以处理不同数量的用户，而无需再进行重新培训。结果表明，A-TCNN实现了非常接近测试数据集的网络吞吐量，差距小于3％。还证明，A-TCNN可以获得与两个SOTA基准相当的网络吞吐量，同时最多将运行时降低了三个数量级。

近年来，抑郁症的发病率在全世界迅速上升，但大规模的抑郁症筛查仍然具有挑战性。步态分析提供了抑郁症的非接触，低成本和高效的早期筛查方法。然而，基于步态分析的抑郁症的早期筛查缺乏足够的有效样本数据。在本文中，我们提出了一种用于评估抑郁症风险的骨架数据增强方法。首先，我们提出了五种技术来增加骨架数据并将其应用于抑郁和情感数据集。然后，我们将增强方法分为两种类型（非噪声增强和噪声增强），基于互信息和分类准确性。最后，我们探索了哪些增强策略可以更有效地捕捉人骨架数据的特征。实验结果表明，保留了更多原始骨架数据属性的增强训练数据集确定了检测模型的性能。具体而言，旋转增强和通道掩码增强使抑郁检测精度分别达到92.15％和91.34％。

组织病理组织分类是病理学癌症研究的基本任务。精确区分不同的组织类型是下游研究的好处，如癌症诊断，预后等。现有的作品主要利用计算机视觉中的流行分类骨干，以实现组织病理组织分类。在本文中，我们提出了一种超级轻型即插即用模块，名为金字塔深广阔的学习（PDBL），对于任何训练有素的分类骨架，以进一步提高分类性能而无需重新培训负担。我们模仿病理学家如何观察不同放大率的病理学幻灯片，并为输入图像构造图像金字塔，以获得金字塔内部信息。对于金字塔中的每个级别，我们通过我们提出的深层块（DB-Block）提取多种深度广泛的功能。我们用三个流行的分类骨干网，Shufflenetv2，EppositionNetB0和Reset50配备了PDBL，以评估我们建议模块在两个数据集（Kather Multiclass DataSet和LC25000数据集）上的提出模块的有效性和效率。实验结果表明，所提出的PDBL可以稳定地改善任何CNN骨架的组织级分类性能，特别是对于在训练样本（小于10％）中的小型时，特别是轻量级模型，这极大地节省了计算时间和注释工作。

人体步态是指不仅代表活动能力的每日运动，而且还可以用人类观察者或计算机来识别步行者。最近的研究表明，步态甚至传达了有关沃克情绪的信息。不同情绪状态中的个体可能显示出不同的步态模式。各种情绪和步态模式之间的映射为自动情绪识别提供了新的来源。与传统的情绪检测生物识别技术（例如面部表达，言语和生理参数）相比，步态是可以观察到的，更难以模仿，并且需要从该主题中进行较少的合作。这些优势使步态成为情感检测的有前途的来源。本文回顾了有关基于步态的情绪检测的当前研究，尤其是关于步态参数如何受到不同情绪状态的影响以及如何通过不同的步态模式识别情绪状态的研究。我们专注于情感识别过程中应用的详细方法和技术：数据收集，预处理和分类。最后，我们讨论了使用智能计算和大数据的最先进技术的状态来讨论高效有效的基于步态的情感识别的可能发展。

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.