视觉变压器（VIT）最近在一系列计算机视觉任务中占据了主导地位，但训练数据效率低下，局部语义表示能力较低，而没有适当的电感偏差。卷积神经网络（CNNS）固有地捕获了区域感知语义，激发了研究人员将CNN引入VIT的架构中，以为VIT提供理想的诱导偏见。但是，嵌入在VIT中的微型CNN实现的位置是否足够好？在本文中，我们通过深入探讨混合CNNS/VIT的宏观结构如何增强层次VIT的性能。特别是，我们研究了令牌嵌入层，别名卷积嵌入（CE）的作用，并系统地揭示了CE如何在VIT中注入理想的感应偏置。此外，我们将最佳CE配置应用于最近发布的4个最先进的Vits，从而有效地增强了相应的性能。最后，释放了一个有效的混合CNN/VIT家族，称为CETNET，可以用作通用的视觉骨架。具体而言，CETNET在Imagenet-1K上获得了84.9％的TOP-1准确性（从头开始训练），可可基准上的48.6％的盒子地图和ADE20K上的51.6％MIOU，从而显着提高了相应的最新态度的性能。艺术基线。

我们引入了一个球形指尖传感器进行动态操作。它基于气压压力和飞行时间接近传感器，并且是低延迟，紧凑且身体健壮的。传感器使用训练有素的神经网络根据压力传感器的数据来估计接触位置和三轴接触力，这些数据嵌入了传感器的聚氨酯橡胶范围内。飞行器传感器朝三个不同的外向方向面对，并且一个集成的微控制器样品以200 Hz的速度每个单个传感器。为了量化系统潜伏期对动态操作性能的影响，我们开发和分析了一个称为碰撞脉冲比率的度量，并表征了我们新传感器的端到端潜伏期。我们还向传感器提出了实验演示，包括测量接触过渡，进行粗大映射，与移动物体保持接触力以及避免碰撞的反应。

现代的机器人操纵系统缺乏人类的操纵技巧，部分原因是它们依靠围绕视觉数据的关闭反馈循环，这会降低系统的带宽和速度。通过开发依赖于高带宽力，接触和接近数据的自主握力反射，可以提高整体系统速度和鲁棒性，同时减少对视力数据的依赖。我们正在开发一个围绕低渗透的高速手臂建造的新系统，该系统用敏捷的手指结合了一个高级轨迹计划器，以小于1 Hz的速度运行，低级自主反射控制器的运行量超过300 Hz。我们通过将成功的基线控制器和反射握把控制器的变化的成功抓Grasps的体积和反射系统的体积进行比较，从而表征了反射系统，发现我们的控制器将成功的掌握率与基线相比扩大了55％。我们还使用简单的基于视觉的计划者在自主杂波清除任务中部署了反身抓握控制器，在清除100多个项目的同时，达到了超过90％的成功率。

人行道挑战的数据科学（DSPC）旨在通过提供一个基准的数据集和代码来加速自动化视觉系统，以进行路面状况监测和评估，以创新和开发机器学习算法，这些算法已准备就绪，可以准备好练习。行业使用。比赛的第一版吸引了来自8个国家的22支球队。要求参与者自动检测和分类从多个来源捕获的图像中存在的不同类型的路面遇险，并且在不同的条件下。竞争是以数据为中心的：通过利用各种数据修改方法（例如清洁，标签和增强），团队的任务是提高预定义模型体系结构的准确性。开发了一个实时的在线评估系统，以根据F1分数对团队进行排名。排行榜的结果显示了机器在路面监控和评估中提高自动化的希望和挑战。本文总结了前5个团队的解决方案。这些团队提出了数据清洁，注释，增强和检测参数调整领域的创新。排名最高的团队的F1得分约为0.9。本文以对当前挑战效果很好的不同实验的综述以及对模型准确性的任何显着提高的审查进行了综述。

最近，卷积神经网络（CNN）技术具有普及作为高光谱图像分类（HSIC）的工具。为了在有限样品的条件下提高HSIC的特征提取效率，目前的方法通常使用大量层的深层模型。然而，当样品有限时，深网络模型容易出现过度拟合和梯度消失问题。此外，空间分辨率严重降低，深度深度，这对空间边缘特征提取非常有害。因此，这封信提出了一种HSIC的浅模型，称为深度过度参数化卷积神经网络（DOCNN）。为了确保浅模型的有效提取，引入深度过度参数化卷积（DO-CONV）内核以提取歧视特征。深度过度参数化卷积内核由标准卷积内核和深度卷积内核组成，其可以单独地提取不同信道的空间特征，并同时熔合整个通道的空间特征。此外，为了进一步减少由于卷积操作引起的空间边缘特征的损失，提出了一种密集的残余连接（DRC）结构以适用于整个网络的特征提取部分。从三个基准数据集获得的实验结果表明，该方法在分类准确度和计算效率方面优于其他最先进的方法。

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.

In this chapter, we review and discuss the transformation of AI technology in HCI/UX work and assess how AI technology will change how we do the work. We first discuss how AI can be used to enhance the result of user research and design evaluation. We then discuss how AI technology can be used to enhance HCI/UX design. Finally, we discuss how AI-enabled capabilities can improve UX when users interact with computing systems, applications, and services.

As one of the most important psychic stress reactions, micro-expressions (MEs), are spontaneous and transient facial expressions that can reveal the genuine emotions of human beings. Thus, recognizing MEs (MER) automatically is becoming increasingly crucial in the field of affective computing, and provides essential technical support in lie detection, psychological analysis and other areas. However, the lack of abundant ME data seriously restricts the development of cutting-edge data-driven MER models. Despite the recent efforts of several spontaneous ME datasets to alleviate this problem, it is still a tiny amount of work. To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7,526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years. Afterwards, we adopt four classical spatiotemporal feature learning models on DFME to perform MER experiments to objectively verify the validity of DFME dataset. In addition, we explore different solutions to the class imbalance and key-frame sequence sampling problems in dynamic MER respectively on DFME, so as to provide a valuable reference for future research. The comprehensive experimental results show that our DFME dataset can facilitate the research of automatic MER, and provide a new benchmark for MER. DFME will be published via https://mea-lab-421.github.io.

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.

When using LiDAR semantic segmentation models for safety-critical applications such as autonomous driving, it is essential to understand and improve their robustness with respect to a large range of LiDAR corruptions. In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions. To rigorously evaluate the robustness and generalizability of current approaches, we propose a new benchmark called SemanticKITTI-C, which features 16 out-of-domain LiDAR corruptions in three groups, namely adverse weather, measurement noise and cross-device discrepancy. Then, we systematically investigate 11 LiDAR semantic segmentation models, especially spanning different input representations (e.g., point clouds, voxels, projected images, and etc.), network architectures and training schemes. Through this study, we obtain two insights: 1) We find out that the input representation plays a crucial role in robustness. Specifically, under specific corruptions, different representations perform variously. 2) Although state-of-the-art methods on LiDAR semantic segmentation achieve promising results on clean data, they are less robust when dealing with noisy data. Finally, based on the above observations, we design a robust LiDAR segmentation model (RLSeg) which greatly boosts the robustness with simple but effective modifications. It is promising that our benchmark, comprehensive analysis, and observations can boost future research in robust LiDAR semantic segmentation for safety-critical applications.