Development of guidance, navigation and control frameworks/algorithms for swarms attracted significant attention in recent years. That being said, algorithms for planning swarm allocations/trajectories for engaging with enemy swarms is largely an understudied problem. Although small-scale scenarios can be addressed with tools from differential game theory, existing approaches fail to scale for large-scale multi-agent pursuit evasion (PE) scenarios. In this work, we propose a reinforcement learning (RL) based framework to decompose to large-scale swarm engagement problems into a number of independent multi-agent pursuit-evasion games. We simulate a variety of multi-agent PE scenarios, where finite time capture is guaranteed under certain conditions. The calculated PE statistics are provided as a reward signal to the high level allocation layer, which uses an RL algorithm to allocate controlled swarm units to eliminate enemy swarm units with maximum efficiency. We verify our approach in large-scale swarm-to-swarm engagement simulations.

在未知环境中存在动态障碍的情况下，避免碰撞是无人系统最关键的挑战之一。在本文中，我们提出了一种方法，该方法可以鉴定出椭圆形的障碍，以估计线性和角度障碍速度。我们提出的方法是基于任何对象的概念，可以由椭圆形表示。为了实现这一目标，我们提出了一种基于高斯混合模型，kyachiyan算法和改进算法的变异贝叶斯估计的方法。与现有的基于优化的方法不同，我们提出的方法不需要了解集群数量，并且可以实时操作。此外，我们定义一个基于椭圆形的特征向量以匹配两个及时的接近点帧。我们的方法可以应用于具有静态和动态障碍的任何环境，包括具有旋转障碍的环境。我们将算法与其他聚类方法进行比较，并表明当与轨迹计划器结合时，整体系统可以在存在动态障碍物的情况下有效地穿越未知环境。

多智能体增强学习（Marl）问题通常需要代理商之间的合作，以解决任务。集中化和权力下放是用于玛尔合作的两种方法。虽然由于部分可观测性和非间手性，但易于分散的方法易于收敛到次优解决方案，但涉及集中化的方法遭受可扩展性限制和懒惰的代理问题。集中式培训分散执行范式带出了这两种方法中最好的;然而，集中培训仍然具有可扩展性的上限，而不仅适用于获得的协调性能，而且还具有模型大小和培训时间。在这项工作中，我们采用分散执行范例的集中培训，并调查跨越可变数量的训练型模型的泛化和转移能力。通过特定的MARL问题中的可变数量的代理进行评估，然后对每个训练配置进行可变数量的代理进行贪婪评估来评估此容量。因此，我们分析了培训与评估的代理计数的每个组合的评估性能。我们对捕食者猎物和交通连接环境进行实验评估，并证明可以通过较少的药剂训练获得类似或更高的评估性能。我们得出结论，进行培训的最佳代理商可能与目标代理的数量不同，并且争论在大量代理中的转移可以是比在训练期间直接越来越多的药剂缩放更有效的解决方案。

One of the most efficient methods for model compression is hint distillation, where the student model is injected with information (hints) from several different layers of the teacher model. Although the selection of hint points can drastically alter the compression performance, conventional distillation approaches overlook this fact and use the same hint points as in the early studies. Therefore, we propose a clustering based hint selection methodology, where the layers of teacher model are clustered with respect to several metrics and the cluster centers are used as the hint points. Our method is applicable for any student network, once it is applied on a chosen teacher network. The proposed approach is validated in CIFAR-100 and ImageNet datasets, using various teacher-student pairs and numerous hint distillation methods. Our results show that hint points selected by our algorithm results in superior compression performance compared to state-of-the-art knowledge distillation algorithms on the same student models and datasets.

Object detectors are conventionally trained by a weighted sum of classification and localization losses. Recent studies (e.g., predicting IoU with an auxiliary head, Generalized Focal Loss, Rank & Sort Loss) have shown that forcing these two loss terms to interact with each other in non-conventional ways creates a useful inductive bias and improves performance. Inspired by these works, we focus on the correlation between classification and localization and make two main contributions: (i) We provide an analysis about the effects of correlation between classification and localization tasks in object detectors. We identify why correlation affects the performance of various NMS-based and NMS-free detectors, and we devise measures to evaluate the effect of correlation and use them to analyze common detectors. (ii) Motivated by our observations, e.g., that NMS-free detectors can also benefit from correlation, we propose Correlation Loss, a novel plug-in loss function that improves the performance of various object detectors by directly optimizing correlation coefficients: E.g., Correlation Loss on Sparse R-CNN, an NMS-free method, yields 1.6 AP gain on COCO and 1.8 AP gain on Cityscapes dataset. Our best model on Sparse R-CNN reaches 51.0 AP without test-time augmentation on COCO test-dev, reaching state-of-the-art. Code is available at https://github.com/fehmikahraman/CorrLoss

In this paper, we aim to address the large domain gap between high-resolution face images, e.g., from professional portrait photography, and low-quality surveillance images, e.g., from security cameras. Establishing an identity match between disparate sources like this is a classical surveillance face identification scenario, which continues to be a challenging problem for modern face recognition techniques. To that end, we propose a method that combines face super-resolution, resolution matching, and multi-scale template accumulation to reliably recognize faces from long-range surveillance footage, including from low quality sources. The proposed approach does not require training or fine-tuning on the target dataset of real surveillance images. Extensive experiments show that our proposed method is able to outperform even existing methods fine-tuned to the SCFace dataset.

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given.

The emergence of pre-trained language models (PLMs) has shown great success in many Natural Language Processing (NLP) tasks including text classification. Due to the minimal to no feature engineering required when using these models, PLMs are becoming the de facto choice for any NLP task. However, for domain-specific corpora (e.g., financial, legal, and industrial), fine-tuning a pre-trained model for a specific task has shown to provide a performance improvement. In this paper, we compare the performance of four different PLMs on three public domain-free datasets and a real-world dataset containing domain-specific words, against a simple SVM linear classifier with TFIDF vectorized text. The experimental results on the four datasets show that using PLMs, even fine-tuned, do not provide significant gain over the linear SVM classifier. Hence, we recommend that for text classification tasks, traditional SVM along with careful feature engineering can pro-vide a cheaper and superior performance than PLMs.

这项工作总结了2022年2022年国际生物识别联合会议（IJCB 2022）的IJCB被遮挡的面部识别竞赛（IJCB-OCFR-2022）。OCFR-2022从学术界吸引了总共3支参与的团队。最终，提交了六个有效的意见书，然后由组织者评估。在严重的面部阻塞面前，举行了竞争是为了应对面部识别的挑战。参与者可以自由使用任何培训数据，并且通过使用众所周知的数据集构成面部图像的部分来构建测试数据。提交的解决方案提出了创新，并以所考虑的基线表现出色。这项竞争的主要输出是具有挑战性，现实，多样化且公开可用的遮挡面部识别基准，并具有明确的评估协议。

深度学习（DL）在无线领域中找到了丰富的应用，以提高频谱意识。通常，DL模型要么是根据统计分布后随机初始初始初始初始初始初始初始初始初始初始化，要么在其他数据域（例如计算机视觉）（以转移学习的形式）上进行鉴定，而无需考虑无线信号的唯一特征。即使只有有限的带有标签的培训数据样本，自我监督的学习也能够从射频（RF）信号本身中学习有用的表示形式。我们通过专门制定一组转换以捕获无线信号特征来提出第一个自我监督的RF信号表示学习模型，并将其应用于自动调制识别（AMR）任务。我们表明，通过学习信号表示具有自我监督的学习，可以显着提高样本效率（实现一定准确性性能所需的标记样品数量）。这转化为大量时间和节省成本。此外，与最先进的DL方法相比，自我监管的学习可以提高模型的准确性，即使使用了一小部分训练数据样本，也可以保持高精度。