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.

Point cloud registration (PCR) is a popular research topic in computer vision. Recently, the registration method in an evolutionary way has received continuous attention because of its robustness to the initial pose and flexibility in objective function design. However, most evolving registration methods cannot tackle the local optimum well and they have rarely investigated the success ratio, which implies the probability of not falling into local optima and is closely related to the practicality of the algorithm. Evolutionary multi-task optimization (EMTO) is a widely used paradigm, which can boost exploration capability through knowledge transfer among related tasks. Inspired by this concept, this study proposes a novel evolving registration algorithm via EMTO, where the multi-task configuration is based on the idea of solution space cutting. Concretely, one task searching in cut space assists another task with complex function landscape in escaping from local optima and enhancing successful registration ratio. To reduce unnecessary computational cost, a sparse-to-dense strategy is proposed. In addition, a novel fitness function robust to various overlap rates as well as a problem-specific metric of computational cost is introduced. Compared with 7 evolving registration approaches and 4 traditional registration approaches on the object-scale and scene-scale registration datasets, experimental results demonstrate that the proposed method has superior performances in terms of precision and tackling local optima.

多视图点云注册在3D重建中至关重要。由于从不同角度捕获的点云之间存在密切的连接，因此如果正确利用这些连接，则可以增强注册性能。因此，本文将注册问题建模为多任务优化，并提出了一种新颖的双通道知识共享机制，以有效，有效地解决问题。多视点云注册作为多任务优化的建模是双重的。通过同时考虑两个点云的局部精度以及所涉及的所有点云带来的全局一致性，得出了具有自适应阈值的健身函数。还定义了共同进化搜索过程的框架，以同时优化属于相关任务的多个健身函数。为了提高解决方案质量和收敛速度，拟议的双通道知识共享机制发挥了作用。任务内的知识共享引入了求解更简单的帮助任务，并且在辅助任务和原始任务上共享有用的信息，从而加速了搜索过程。任务间知识共享探讨了原始任务中埋葬的共同点，旨在防止任务陷入本地Optima。在模型对象以及场景点云上进行的综合实验显示了所提出的方法的功效。

图形神经网络（GNNS）将深度神经网络（DNN）的成功扩展到非欧几里德图数据，实现了各种任务的接地性能，例如节点分类和图形属性预测。尽管如此，现有系统效率低，培训数十亿节点和GPU的节点和边缘训练大图。主要瓶颈是准备GPU数据的过程 - 子图采样和特征检索。本文提出了一个分布式GNN培训系统的BGL，旨在解决一些关键思想的瓶颈。首先，我们提出了一种动态缓存引擎，以最小化特征检索流量。通过协同设计缓存政策和抽样顺序，我们发现低开销和高缓存命中率的精美斑点。其次，我们改善了曲线图分区算法，以减少子图采样期间的交叉分区通信。最后，仔细资源隔离减少了不同数据预处理阶段之间的争用。关于各种GNN模型和大图数据集的广泛实验表明，BGL平均明显优于现有的GNN训练系统20.68倍。

5G毫米波（MMWAVE）信号与传播信道和传播环境具有固有的几何连接。因此，它们可用于共同本地化接收器并映射传播环境，该传播环境被称为同时定位和映射（SLAM）。5G SLAM中最重要的任务之一是处理测量模型的非线性。为了解决这个问题，现有的5G SLAM依赖于Sigma点或扩展卡尔曼滤波器，针对现有概率密度函数（PDF）线性化测量功能。在本文中，我们研究了关于后部PDF的测量功能的线性化，并将迭代后线性化滤波器实施到泊松多Bernoulli Slam滤波器中。仿真结果表明了所得SLAM过滤器的精度和精确改善。

面部反欺骗（FAS）在防止演示攻击中的人脸识别系统中起着至关重要的作用。由于身份和微不足道的方差不足，现有面部反欺骗数据集缺乏多样性，这限制了FAS模型的泛化能力。在本文中，我们提出了双重欺骗解散生成（DSDG）框架，通过“通过生成反欺骗”来解决这一挑战。根据变形AutiaceDer（VAE）中的可解释分解潜在解剖学，DSDG学习身份表示的联合分布和潜在空间中的欺骗模式表示。然后，可以从随机噪声生成大规模成对的实时和欺骗图像，以提高训练集的分集。然而，由于VAE的固有缺陷，一些产生的面部图像被部分地扭曲。这种嘈杂的样本很难预测精确的深度值，因此可能阻碍广泛使用的深度监督优化。为了解决这个问题，我们进一步引入了轻量级深度不确定性模块（DUM），减轻了噪声样本对深度不确定性学习的不利影响。 DUM在没有依赖性的情况下开发，因此可以灵活地集成与任何深度监督网络进行面部反欺骗。我们评估了提出的方法在五个流行基准上的有效性，并在测试中实现了最先进的结果。该代码可在https://github.com/jdai-cv/facex-zoo/tree/main/addition_module/dsdg中获得。

Panoptic Part Segmentation (PPS) unifies panoptic segmentation and part segmentation into one task. Previous works utilize separated approaches to handle thing, stuff, and part predictions without shared computation and task association. We aim to unify these tasks at the architectural level, designing the first end-to-end unified framework named Panoptic-PartFormer. Moreover, we find the previous metric PartPQ biases to PQ. To handle both issues, we make the following contributions: Firstly, we design a meta-architecture that decouples part feature and things/stuff feature, respectively. We model things, stuff, and parts as object queries and directly learn to optimize all three forms of prediction as a unified mask prediction and classification problem. We term our model as Panoptic-PartFormer. Secondly, we propose a new metric Part-Whole Quality (PWQ) to better measure such task from both pixel-region and part-whole perspectives. It can also decouple the error for part segmentation and panoptic segmentation. Thirdly, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross attention scheme to further boost part segmentation qualities. We design a new part-whole interaction method using masked cross attention. Finally, the extensive ablation studies and analysis demonstrate the effectiveness of both Panoptic-PartFormer and Panoptic-PartFormer++. Compared with previous Panoptic-PartFormer, our Panoptic-PartFormer++ achieves 2% PartPQ and 3% PWQ improvements on the Cityscapes PPS dataset and 5% PartPQ on the Pascal Context PPS dataset. On both datasets, Panoptic-PartFormer++ achieves new state-of-the-art results with a significant cost drop of 70% on GFlops and 50% on parameters. Our models can serve as a strong baseline and aid future research in PPS. Code will be available.

The explosion of e-commerce has caused the need for processing and analysis of product titles, like entity typing in product titles. However, the rapid activity in e-commerce has led to the rapid emergence of new entities, which is difficult to be solved by general entity typing. Besides, product titles in e-commerce have very different language styles from text data in general domain. In order to handle new entities in product titles and address the special language styles problem of product titles in e-commerce domain, we propose our textual entailment model with continuous prompt tuning based hypotheses and fusion embeddings for e-commerce entity typing. First, we reformulate the entity typing task into a textual entailment problem to handle new entities that are not present during training. Second, we design a model to automatically generate textual entailment hypotheses using a continuous prompt tuning method, which can generate better textual entailment hypotheses without manual design. Third, we utilize the fusion embeddings of BERT embedding and CharacterBERT embedding with a two-layer MLP classifier to solve the problem that the language styles of product titles in e-commerce are different from that of general domain. To analyze the effect of each contribution, we compare the performance of entity typing and textual entailment model, and conduct ablation studies on continuous prompt tuning and fusion embeddings. We also evaluate the impact of different prompt template initialization for the continuous prompt tuning. We show our proposed model improves the average F1 score by around 2% compared to the baseline BERT entity typing model.

Active learning enables efficient model training by leveraging interactions between machine learning agents and human annotators. We study and propose a novel framework that formulates batch active learning from the sparse approximation's perspective. Our active learning method aims to find an informative subset from the unlabeled data pool such that the corresponding training loss function approximates its full data pool counterpart. We realize the framework as sparsity-constrained discontinuous optimization problems, which explicitly balance uncertainty and representation for large-scale applications and could be solved by greedy or proximal iterative hard thresholding algorithms. The proposed method can adapt to various settings, including both Bayesian and non-Bayesian neural networks. Numerical experiments show that our work achieves competitive performance across different settings with lower computational complexity.

人脸图像通常以广泛的视觉量表出现。现有的面部表示通过组装有限系列的预定尺度的多尺度方案来追求处理量表变化的带宽。这种多弹药方案带来了推理负担，而预定义的量表不可避免地从真实数据中差异。取而代之的是，从数据中学习比例参数，并将其用于单发功能推理是一个不错的解决方案。为此，我们通过诉诸规模空间理论并实现两倍的设施来改革Conv层：1）Conv层从真实数据分布中学习一组尺度，每个数据分布都由Conv内核来实现； 2）该图层自动在适当的通道和位置上突出显示与输入模式量表及其存在相对应的位置。然后，我们通过堆叠改革层的层来实现分层尺度的关注，建立一种名为“比例尺注意Cons Neurnet网络”（\ textbf {scan-cnn}）的新颖风格。我们将扫描CNN应用于面部识别任务，并推动SOTA性能的前沿。当面部图像模糊时，准确性增长更为明显。同时，作为单发方案，该推断比多弹性融合更有效。与普通CNN相比，制造了一组工具，以确保对扫描CNN进行快速训练和推理成本的零增加。