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

自我监督图像生成中普遍的方法是在像素级表示上操作。尽管这种方法可以产生高质量的图像，但它不能从矢量化的简单性和先天质量中受益。在这里，我们提出了一个以图像的冲程级表示作用的绘图代理。在每个时间步骤中，代理商首先评估当前画布，并决定是停止还是继续绘画。当做出“抽奖”决定时，代理输出一个程序，指示要绘制的中风。结果，它通过使用最小数量的笔触并动态决定何时停止，从而产生最终的栅格图像。我们通过对MNIST和Omniglot数据集进行强化学习来培训我们的代理，以无条件生成和解析（重建）任务。我们利用我们的解析代理在Omniglot挑战中进行典范生成和类型的条件概念生成，而无需进行任何进一步的培训。我们在所有三代任务和解析任务上提供了成功的结果。至关重要的是，我们不需要任何中风级别或矢量监督；我们只使用栅格图像进行训练。

我们提出了streamDeq，这是一种以最小为每片计算的视频中框架表示的方法。与传统方法至少随着网络深度线性线性增长的常规方法相反，我们旨在以连续的方式更新表示形式。为此，我们利用最近出现的隐式层模型，该模型通过解决固定点问题来扩展图像的表示。我们的主要见解是利用视频的缓慢变化，并使用先前的框架表示作为每个帧的初始条件。该方案有效地回收了最近的推理计算，并大大减少了所需的处理时间。通过广泛的实验分析，我们表明StreamDeq能够在几个帧时间内恢复近乎最佳的表示形式，并在整个视频持续时间内保持最新的表示。我们在视频语义细分和视频对象检测方面进行的实验表明，StreamDeq以基线（标准MDEQ）的准确度达到了准确性，而$ 3 \ times $ $ abter $ abter的$ 3。项目页面可在以下网址获得：https：//ufukertenli.github.io/streamdeq/

本文介绍了Houghnet，这是一种单阶段，无锚，基于投票的，自下而上的对象检测方法。受到广义的霍夫变换的启发，霍尼特通过在该位置投票的总和确定了某个位置的物体的存在。投票是根据对数极极投票领域的近距离和长距离地点收集的。由于这种投票机制，Houghnet能够整合近距离和远程的班级条件证据以进行视觉识别，从而概括和增强当前的对象检测方法，这通常仅依赖于本地证据。在可可数据集中，Houghnet的最佳型号达到$ 46.4 $ $ $ ap $（和$ 65.1 $ $ $ ap_ {50} $），与自下而上的对象检测中的最先进的作品相同，超越了最重要的一项 - 阶段和两阶段方法。我们进一步验证了提案在其他视觉检测任务中的有效性，即视频对象检测，实例分割，3D对象检测和人为姿势估计的关键点检测以及其他“图像”图像生成任务的附加“标签”，其中集成的集成在所有情况下，我们的投票模块始终提高性能。代码可在https://github.com/nerminsamet/houghnet上找到。

在这项研究中，我们介绍了机器感知的措施，灵感来自于人类感知的概念（JND）的概念。基于该措施，我们提出了一种对抗性图像生成算法，其通过添加剂噪声迭代地扭曲图像，直到模型通过输出错误标签来检测图像中的变化。添加到原始图像的噪声被定义为模型成本函数的梯度。定义了一种新的成本函数，以明确地最小化应用于输入图像的扰动量，同时强制执行对抗和输入图像之间的感知相似性。为此目的，经过众所周知的总变化和有界范围术语来规范成本函数，以满足对抗图像的自然外观。我们评估我们的算法在CiFar10，ImageNet和MS Coco Datasets上定性和定量地生成的对抗性图像。我们对图像分类和对象检测任务的实验表明，通过我们的JND方法产生的对抗性图像在欺骗识别/检测模型以及与由最先进的方法产生的图像相比，扰动扰动，即， FGV，FSGM和Deepfool方法。

尽管广泛用作可视检测任务的性能措施，但平均精度（AP）In（i）的限制在反映了本地化质量，（ii）对其计算的设计选择的鲁棒性以及其对输出的适用性没有信心分数。 Panoptic质量（PQ），提出评估Panoptic Seationation（Kirillov等，2019）的措施，不会遭受这些限制，而是限于Panoptic Seationation。在本文中，我们提出了基于其本地化和分类质量的视觉检测器的平均匹配误差，提出了定位召回精度（LRP）误差。 LRP错误，最初仅为Oksuz等人进行对象检测。 （2018），不遭受上述限制，适用于所有视觉检测任务。我们还介绍了最佳LRP（OLRP）错误，因为通过置信区获得的最小LRP错误以评估视觉检测器并获得部署的最佳阈值。我们提供对AP和PQ的LRP误差的详细比较分析，并使用七个可视检测任务（即对象检测，关键点检测，实例分割，Panoptic分段，视觉关系检测，使用近100个最先进的视觉检测器零拍摄检测和广义零拍摄检测）使用10个数据集来统一地显示LRP误差提供比其对应物更丰富和更辨别的信息。可用的代码：https：//github.com/kemaloksuz/lrp-error

This paper expounds the design and control of a new Variable Stiffness Series Elastic Actuator (VSSEA). It is established by employing a modular mechanical design approach that allows us to effectively optimise the stiffness modulation characteristics and power density of the actuator. The proposed VSSEA possesses the following features: i) no limitation in the work-range of output link, ii) a wide range of stiffness modulation (~20Nm/rad to ~1KNm/rad), iii) low-energy-cost stiffness modulation at equilibrium and non-equilibrium positions, iv) compact design and high torque density (~36Nm/kg), and v) high-speed stiffness modulation (~3000Nm/rad/s). Such features can help boost the safety and performance of many advanced robotic systems, e.g., a cobot that physically interacts with unstructured environments and an exoskeleton that provides physical assistance to human users. These features can also enable us to utilise variable stiffness property to attain various regulation and trajectory tracking control tasks only by employing conventional controllers, eliminating the need for synthesising complex motion control systems in compliant actuation. To this end, it is experimentally demonstrated that the proposed VSSEA is capable of precisely tracking desired position and force control references through the use of conventional Proportional-Integral-Derivative (PID) controllers.

Animals run robustly in diverse terrain. This locomotion robustness is puzzling because axon conduction velocity is limited to a few ten meters per second. If reflex loops deliver sensory information with significant delays, one would expect a destabilizing effect on sensorimotor control. Hence, an alternative explanation describes a hierarchical structure of low-level adaptive mechanics and high-level sensorimotor control to help mitigate the effects of transmission delays. Motivated by the concept of an adaptive mechanism triggering an immediate response, we developed a tunable physical damper system. Our mechanism combines a tendon with adjustable slackness connected to a physical damper. The slack damper allows adjustment of damping force, onset timing, effective stroke, and energy dissipation. We characterize the slack damper mechanism mounted to a legged robot controlled in open-loop mode. The robot hops vertically and planar over varying terrains and perturbations. During forward hopping, slack-based damping improves faster perturbation recovery (up to 170%) at higher energetic cost (27%). The tunable slack mechanism auto-engages the damper during perturbations, leading to a perturbation-trigger damping, improving robustness at minimum energetic cost. With the results from the slack damper mechanism, we propose a new functional interpretation of animals' redundant muscle tendons as tunable dampers.

Generative models learned from training using deep learning methods can be used as priors in inverse under-determined inverse problems, including imaging from sparse set of measurements. In this paper, we present a novel hierarchical deep-generative model MrSARP for SAR imagery that can synthesize SAR images of a target at different resolutions jointly. MrSARP is trained in conjunction with a critic that scores multi resolution images jointly to decide if they are realistic images of a target at different resolutions. We show how this deep generative model can be used to retrieve the high spatial resolution image from low resolution images of the same target. The cost function of the generator is modified to improve its capability to retrieve the input parameters for a given set of resolution images. We evaluate the model's performance using the three standard error metrics used for evaluating super-resolution performance on simulated data and compare it to upsampling and sparsity based image sharpening approaches.

Measuring and monitoring soil organic carbon is critical for agricultural productivity and for addressing critical environmental problems. Soil organic carbon not only enriches nutrition in soil, but also has a gamut of co-benefits such as improving water storage and limiting physical erosion. Despite a litany of work in soil organic carbon estimation, current approaches do not generalize well across soil conditions and management practices. We empirically show that explicit modeling of cause-and-effect relationships among the soil processes improves the out-of-distribution generalizability of prediction models. We provide a comparative analysis of soil organic carbon estimation models where the skeleton is estimated using causal discovery methods. Our framework provide an average improvement of 81% in test mean squared error and 52% in test mean absolute error.