本文介绍了Speakin团队提交的SPEAKER验证（SV）系统，该系统针对2022年远场演讲者验证挑战（FFSVC2022）的任务2和任务2。挑战的SV任务集中在完全监督的远场演讲者验证（任务1）和半监督远场扬声器验证（任务2）的问题上。在任务1中，我们将Voxceleb和FFSVC2020数据集用作火车数据集。对于任务2，我们仅将Voxceleb数据集用作火车集。为此挑战开发了基于重新连接和基于REPVGG的架构。全局统计池结构和MQMHA池结构用于跨时间汇总框架级特征，以获得语音级别的表示。我们采用了Am-Softmax和Aam-Softmax来对产生的嵌入进行分类。我们创新提出了一种分阶段的转移学习方法。在训练阶段，我们保留扬声器的权重，并且在此阶段没有积极的样本来训练它们。然后，我们在第二阶段用正面和负样品微调这些权重。与传统的转移学习策略相比，该策略可以更好地改善模型性能。亚均值和标志的后端方法用于解决域不匹配的问题。在融合阶段，任务1中融合了三个模型，并在任务2中融合了两个模型。在FFSVC2022排行榜上，我们提交的EER为3.0049％，在Task1中，相应的MindCF为0.2938。在任务2中，EER和MindCF分别为6.2060％和0.5232。我们的方法可以提高表现出色，并在两项挑战任务中排名第一。

智能药物输送手推车是一种先进的智能药物输送设备。与传统的手动药物输送相比，它具有较高的药物输送效率和较低的错误率。在这个项目中，设计和制造了一款智能的药车，可以通过视觉识别技术识别道路路线和目标病房的房间数量。手推车根据已确定的房间数选择相应的途径，将药物准确地运送到目标病房，并在输送药物后返回药房。智能药物输送车使用直流电源，电动机驱动模块控制两个直流电动机，这克服了转弯角度过度偏差的问题。手推车线检查功能使用闭环控制来提高线路检查的准确性和手推车速度的可控性。病房号的识别由摄像机模块使用微控制器完成，并且具有自适应调整环境亮度，失真校正，自动校准等的功能。蓝牙模块实现了两个合作药物交付车之间的通信，该模块实现了高效，准确的沟通和互动。实验表明，智能毒品输送车可以准确地识别房间的数量，并计划将毒品运送到远处，中间和附近病房的路线，并具有快速和准确的判断的特征。此外，有两个药车可以合作，以高效率和高合作的方式向同一病房运送药物。

基于注意力机制的变压器在各个领域取得了令人印象深刻的成功。但是，注意机制具有二次复杂性，严重阻碍了变形金刚处理众多令牌并扩展到更大的模型。先前的方法主要利用矩阵乘法的相似性分解和关联性来设计线性时间注意机制。它们通过重新引入归纳偏见（例如位置）来避免关注对微不足道的分布，从而以模型的一般性和表达性为代价。在本文中，我们将基于流网络理论的特定电感偏差线性化。我们引起人们的注意，因为信息流从源（值）汇总到水槽（结果）通过学习的流动能力（结果）（注意）。在此框架内，我们将流量保护的特性应用于注意力，并提出线性复杂性的流意见机制。通过分别保留用于源竞争的水槽的传入流以及水槽分配的传出流，流动意见固有地产生了信息的关注，而无需使用特定的电感偏见。流动性授权，流动形式在线性时间内的范围内表现出色，包括长序列，时间序列，视觉，自然语言和强化学习。代码和设置可在此存储库中获得：https：//github.com/thuml/flowformer。

这项工作旨在通过使用路边激光射击环境的3D感知来应对自动驾驶的挑战。我们设计了一个3D对象检测模型，该模型可以实时检测路边激光雷达的交通参与者。我们的模型使用现有的3D检测器作为基线并提高其准确性。为了证明我们提出的模块的有效性，我们在三个不同的车辆和基础设施数据集上训练和评估模型。为了显示我们探测器的域适应能力，我们在来自中国的基础架构数据集上训练它，并在德国记录的其他数据集上进行转移学习。我们为检测器中每个模块进行几套实验和消融研究，这些实验表明我们的模型的表现优于基线，而推理速度为45 Hz（22 ms）。我们对基于激光雷达的3D探测器做出了重大贡献，可用于智能城市应用程序，以提供连接和自动化的车辆具有深远的视野。连接到路边传感器的车辆可以获取有关拐角处其他车辆的信息，以改善其道路和操纵计划并提高道路交通安全性。

动态状态表示学习是机器人学习中的重要任务。可以捕获动力学信息的潜在空间在加速模型的自由强化学习，缩小模拟到现实差距以及降低运动计划的复杂性等领域中具有广泛的应用。但是，当前的动态状态表示方法在复杂的动态系统（例如可变形对象）上的扩展很差，并且不能将良好定义的仿真函数直接嵌入到训练管道中。我们提出了DIFFSRL，这是一种动态状态表示学习管道，利用可区分的模拟可以将复杂的动力学模型嵌入到端到端训练的一部分。我们还将可区分的动态约束作为管道的一部分集成，这为潜在状态提供了意识到动态约束的激励措施。我们进一步建立了在软体体模拟系统PlastonElab上学习基准的国家表示基准，我们的模型在捕获长期动态和奖励预测方面表现出了卓越的性能。

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.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

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.

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.

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.