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。

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.

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.

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.

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。

现有的LIDAR基金​​标记系统具有使用限制。特别是，利达塔格（Lidartag）需要特定的标记放置和基于图像的激光雷达基准标记，要求从一个角度对点云进行采样。结果，随着点云从多个角度采样，基准标记的检测仍然是一个未解决的问题。在这封信中，我们开发了一种新颖的算法来检测多视点云中的基准标记。提出的算法包括两个阶段。首先，感兴趣的区域（ROI）检测发现可能包含基准标记的点簇。具体而言，由于从空间的角度来看，从强度的角度提取ROI的方法是引入的，即从空间角度来看，标记是纸张或薄板的床单，与它们所连接的平面是不可区分的。其次，标记检测验证候选ROI是否包含基金标记，并输出有效ROI中标记的ID号和顶点位置。特别是，将ROI传输到预定义的中间平面，目的是采用球形投影以生成强度图像，然后通过强度图像完成标记检测。提供定性和定量实验结果以验证所提出的算法。代码和结果可在以下网址获得：https：//github.com/york-sdcnlab/marker?detection-general

电子设计自动化（EDA）社区一直在积极探索非常大规模的计算机辅助设计（VLSI CAD）的机器学习。许多研究探索了基于学习的技术，用于设计流中的跨阶段预测任务，以实现更快的设计收敛。尽管建筑机器学习（ML）模型通常需要大量数据，但由于缺乏大型公共数据集，大多数研究只能生成小型内部数据集进行验证。在本文中，我们介绍了第一个用于机器学习任务的开源数据集，称为CircuitNet。该数据集由基于6种开源RISC-V设计的商业设计工具的多功能运行中提取的10K以上样品组成。

最近，学习的视频压缩引起了很多关注，并显示出令人鼓舞的结果的快速发展趋势。但是，先前的作品仍然存在一些批评问题，并且在广泛使用的PSNR度量方面，具有传统压缩标准的性​​能差距。在本文中，我们提出了几种技术来有效提高性能。首先，为了解决累积错误的问题，我们将有条件的I框架作为GOP中的第一帧，该框架稳定了重建的质量并节省了比特率。其次，为了有效地提高相互预测的准确性而不增加解码器的复杂性，我们提出了一种像素到功能的运动预测方法，可以帮助我们获得高质量的运动信息。第三，我们提出了一种基于概率的熵跳过方法，该方法不仅带来了性能增长，而且大大降低了熵编码的运行时。借助这些强大的技术，本文提出了Alphavc，这是一种高性能且高效的学习视频压缩方案。据我们所知，Alphavc是第一个E2E AI编解码器，它超过了PSNR的所有常见测试数据集上最新的压缩标准VVC（-28.2％BD率节省）和MSSSSIM（-52.2％BD-rate节省），并且具有非常快速的编码（0.001x VVC）和解码（1.69x VVC）速度。