傅立叶Ptychographic显微镜（FPM）是一种成像过程，它通过计算平均值克服了传统的传统显微镜空间带宽产品（SBP）的限制。它利用使用低数值孔径（NA）物镜捕获的多个图像，并通过频域缝线实现高分辨率相成像。现有的FPM重建方法可以广泛地分为两种方法：基于迭代优化的方法，这些方法基于正向成像模型的物理学以及通常采用馈送深度学习框架的数据驱动方法。我们提出了一个混合模型驱动的残留网络，该网络将远期成像系统的知识与深度数据驱动的网络相结合。我们提出的架构LWGNET将传统的电线流优化算法展开为一种新型的神经网络设计，该设计通过复杂的卷积块增强了梯度图像。与其他传统的展开技术不同，LWGNET在PAR上执行时使用的阶段较少，甚至比现有的传统和深度学习技术更好，尤其是对于低成本和低动态范围CMOS传感器。低位深度和低成本传感器的性能提高有可能显着降低FPM成像设置的成本。最后，我们在收集到的实际数据上显示出始终提高的性能。

Large training data and expensive model tweaking are standard features of deep learning for images. As a result, data owners often utilize cloud resources to develop large-scale complex models, which raises privacy concerns. Existing solutions are either too expensive to be practical or do not sufficiently protect the confidentiality of data and models. In this paper, we study and compare novel \emph{image disguising} mechanisms, DisguisedNets and InstaHide, aiming to achieve a better trade-off among the level of protection for outsourced DNN model training, the expenses, and the utility of data. DisguisedNets are novel combinations of image blocktization, block-level random permutation, and two block-level secure transformations: random multidimensional projection (RMT) and AES pixel-level encryption (AES). InstaHide is an image mixup and random pixel flipping technique \cite{huang20}. We have analyzed and evaluated them under a multi-level threat model. RMT provides a better security guarantee than InstaHide, under the Level-1 adversarial knowledge with well-preserved model quality. In contrast, AES provides a security guarantee under the Level-2 adversarial knowledge, but it may affect model quality more. The unique features of image disguising also help us to protect models from model-targeted attacks. We have done an extensive experimental evaluation to understand how these methods work in different settings for different datasets.

When robots interact with humans in homes, roads, or factories the human's behavior often changes in response to the robot. Non-stationary humans are challenging for robot learners: actions the robot has learned to coordinate with the original human may fail after the human adapts to the robot. In this paper we introduce an algorithmic formalism that enables robots (i.e., ego agents) to co-adapt alongside dynamic humans (i.e., other agents) using only the robot's low-level states, actions, and rewards. A core challenge is that humans not only react to the robot's behavior, but the way in which humans react inevitably changes both over time and between users. To deal with this challenge, our insight is that -- instead of building an exact model of the human -- robots can learn and reason over high-level representations of the human's policy and policy dynamics. Applying this insight we develop RILI: Robustly Influencing Latent Intent. RILI first embeds low-level robot observations into predictions of the human's latent strategy and strategy dynamics. Next, RILI harnesses these predictions to select actions that influence the adaptive human towards advantageous, high reward behaviors over repeated interactions. We demonstrate that -- given RILI's measured performance with users sampled from an underlying distribution -- we can probabilistically bound RILI's expected performance across new humans sampled from the same distribution. Our simulated experiments compare RILI to state-of-the-art representation and reinforcement learning baselines, and show that RILI better learns to coordinate with imperfect, noisy, and time-varying agents. Finally, we conduct two user studies where RILI co-adapts alongside actual humans in a game of tag and a tower-building task. See videos of our user studies here: https://youtu.be/WYGO5amDXbQ

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.

Tasks critical to enterprise profitability, such as customer churn prediction, fraudulent account detection or customer lifetime value estimation, are often tackled by models trained on features engineered from customer data in tabular format. Application-specific feature engineering adds development, operationalization and maintenance costs over time. Recent advances in representation learning present an opportunity to simplify and generalize feature engineering across applications. When applying these advancements to tabular data researchers deal with data heterogeneity, variations in customer engagement history or the sheer volume of enterprise datasets. In this paper, we propose a novel approach to encode tabular data containing customer transactions, purchase history and other interactions into a generic representation of a customer's association with the business. We then evaluate these embeddings as features to train multiple models spanning a variety of applications. CASPR, Customer Activity Sequence-based Prediction and Representation, applies Transformer architecture to encode activity sequences to improve model performance and avoid bespoke feature engineering across applications. Our experiments at scale validate CASPR for both small and large enterprise applications.

We propose a light-weight and highly efficient Joint Detection and Tracking pipeline for the task of Multi-Object Tracking using a fully-transformer architecture. It is a modified version of TransTrack, which overcomes the computational bottleneck associated with its design, and at the same time, achieves state-of-the-art MOTA score of 73.20%. The model design is driven by a transformer based backbone instead of CNN, which is highly scalable with the input resolution. We also propose a drop-in replacement for Feed Forward Network of transformer encoder layer, by using Butterfly Transform Operation to perform channel fusion and depth-wise convolution to learn spatial context within the feature maps, otherwise missing within the attention maps of the transformer. As a result of our modifications, we reduce the overall model size of TransTrack by 58.73% and the complexity by 78.72%. Therefore, we expect our design to provide novel perspectives for architecture optimization in future research related to multi-object tracking.

在科学计算的许多领域越来越流行的人工神经网络（ANN）的大量使用迅速增加了现代高性能计算系统的能源消耗。新型的神经形态范式提供了一种吸引人的替代方案，它直接在硬件中实施了ANN。但是，对于科学计算中用例使用ANN在神经形态硬件上运行ANN的实际好处知之甚少。在这里，我们提出了一种方法，用于测量使用常规硬件的ANN来计算推理任务的时间。此外，我们为这些任务设计了一个体系结构，并根据最先进的模拟内存计算（AIMC）平台估算了相同的指标，这是神经形态计算中的关键范例之一。在二维凝结物质系统中的量子多体物理学中的用例比较两种方法，并在粒子物理学中大型强子对撞机上以40 MHz的速率以40 MHz的速率进行异常检测。我们发现，与传统硬件相比，AIMC最多可以达到一个较短的计算时间，最高三个数量级的能源成本。这表明使用神经形态硬件进行更快，更可持续的科学计算的潜力。

听觉对于自动驾驶汽车（AV）至关重要，以更好地感知其周围环境。尽管相机，激光雷达和雷达等AV的视觉传感器有助于看到其周围环境，但AV无法看到这些传感器的视线。另一方面，视线无法阻碍AV的听力感。例如，即使紧急车辆不在AV的视线之内，AV也可以通过音频分类识别紧急车辆的警笛。因此，听觉感知与相机，激光雷达和基于雷达的感知系统互补。本文提出了一个基于深度学习的强大音频分类框架，旨在提高对AV的环境感知。提出的框架利用深度卷积神经网络（CNN）来对不同的音频类进行分类。 Urbansound8K是一个城市环境数据集，用于训练和测试开发的框架。七个音频课程，即空调，汽车喇叭，儿童播放，狗皮，发动机空闲，枪声和警报器，是从urbansound8k数据集中识别的，因为它们与AVS相关。我们的框架可以以97.82％的精度对不同的音频类别进行分类。此外，介绍了所有十个类的音频分类精度，这证明，与现有的音频分类框架相比，在与AV相关的声音的情况下，我们的框架的性能更好。

人口贩运是一个普遍的问题，尽管在全球范围内为与之作斗争，但仍坚持不懈。任何年龄，种族，种族，性别，性别认同，性取向，国籍，移民身份，文化背景，宗教，社会经济阶级和教育的个人都可以成为人口贩运的受害者。随着技术的进步和引入自动驾驶汽车（AVS），人口贩子将采用新的方式运输受害者，这可以加速有组织的人口贩运网络的增长，这可以使对执法人员更具挑战性的人口贩运的探测机构。这项研究的目的是为自动驾驶汽车开发基于创新的音频分析的人口贩运检测框架。这项研究的主要贡献是：（i）为AVS定义四个非平凡，可行和现实的人口贩运情景； （ii）创建一个与人口贩运有关的新的，全面的音频数据集，其中五个类别，即哭泣，尖叫，车门爆炸，汽车噪音和对话； （iii）开发一个与人口贩运有关的音频数据分类的深1D卷积神经网络（CNN）体系结构。我们还使用新的音频数据集进行了案例研究，并评估了深1-D CNN的音频分类性能。我们的分析表明，深1-D CNN可以将来自人口贩运受害者的声音与非人口贩运声音的准确性为95％，这证明了我们框架的功效。

3D服装重建的现有方法要么假设服装几何形状的预定义模板（将其限制为固定服装样式），要么产生顶点有色网眼（缺少高频纹理细节）。我们的新型框架共同学习的几何和语义信息来自输入单眼图像，用于无模板纹理的3D服装数字化。更具体地说，我们建议扩展去皮的表示，以预测像素对齐的分层深度和语义图以提取3D服装。进一步利用分层表示，以参数化提取服装的任意表面，而没有任何人类干预以形成紫外线图集。然后，通过将像素从输入图像从输入图像投射到可见区域的UV空间，然后以混合方式将纹理以混合方式赋予，然后添加封闭的区域。因此，我们能够将任意放松的衣服样式数字化，同时从单眼图像中保留高频纹理细节。我们在三个公开可用的数据集中获得了高保真3D服装重建结果，并在Internet图像上概括。