Thanks to the development of 2D keypoint detectors, monocular 3D human pose estimation (HPE) via 2D-to-3D uplifting approaches have achieved remarkable improvements. Still, monocular 3D HPE is a challenging problem due to the inherent depth ambiguities and occlusions. To handle this problem, many previous works exploit temporal information to mitigate such difficulties. However, there are many real-world applications where frame sequences are not accessible. This paper focuses on reconstructing a 3D pose from a single 2D keypoint detection. Rather than exploiting temporal information, we alleviate the depth ambiguity by generating multiple 3D pose candidates which can be mapped to an identical 2D keypoint. We build a novel diffusion-based framework to effectively sample diverse 3D poses from an off-the-shelf 2D detector. By considering the correlation between human joints by replacing the conventional denoising U-Net with graph convolutional network, our approach accomplishes further performance improvements. We evaluate our method on the widely adopted Human3.6M and HumanEva-I datasets. Comprehensive experiments are conducted to prove the efficacy of the proposed method, and they confirm that our model outperforms state-of-the-art multi-hypothesis 3D HPE methods.

本文介绍了一个分散的多代理轨迹计划（MATP）算法，该算法保证在有限的沟通范围内在障碍物丰富的环境中生成安全，无僵硬的轨迹。所提出的算法利用基于网格的多代理路径计划（MAPP）算法进行僵局，我们引入了子目标优化方法，使代理会收敛到从MAPP生成的无僵局生成的路点。此外，提出的算法通过采用线性安全走廊（LSC）来确保优化问题和避免碰撞的可行性。我们验证所提出的算法不会在随机森林和密集的迷宫中造成僵局，而不论沟通范围如何，并且在飞行时间和距离方面的表现都优于我们以前的工作。我们通过使用十个四肢的硬件演示来验证提出的算法。

本文提出了一种以完全分布式方式工作的协同环境学习算法。多机器人系统比单个机器人更有效，但它涉及以下挑战：1）使用多个机器人在线分布式学习环境地图; 2）基于学习地图的安全和有效的探索路径的产生; 3）对机器人数量的维持能力。为此，我们将整个过程划分为环境学习和路径规划的两个阶段。在每个阶段应用分布式算法并通过相邻机器人之间的通信组合。环境学习算法使用分布式高斯过程，路径规划算法使用分布式蒙特卡罗树搜索。因此，我们构建一个可扩展系统，而无需对机器人数量的约束。仿真结果证明了所提出的系统的性能和可扩展性。此外，基于实际数据集的仿真验证了我们算法在更现实的方案中的实用程序。

由于多重冲突目标和非凸起约束上升的数值问题，快速生成无人机的最佳追逐动态，以遵循障碍物之间的动态目标是挑战。本研究建议解决具有融合的快速可靠的管道的困难，该管道包含1）目标运动预测和2）追逐计划者。它们基于采样和检查方法，包括生成高质量候选基元和具有光计算负荷的可行性测试。我们通过选择由过去观察构建的一组候选者中选择最佳预测来预测目标的运动。基于预测，我们构建了一组预期追逐轨迹，其减少了高阶导数，同时从预测的目标运动保持所需的相对距离。然后，候选轨迹在追逐者的安全性和朝向目标的可视性上进行测试，而不会逼近约束。在涉及动态障碍的具有挑战性的情况下，彻底评估了所提出的算法。此外，从目标识别到追逐运动规划的整体过程在无人机上完全实施，展示了现实世界的适用性。

本文介绍了一个新的在线多代理轨迹规划算法，可确保在杂乱的环境中产生安全，动态可行的轨迹。所提出的算法利用线性安全走廊（LSC）来制定分布式轨迹优化问题，只有可行的约束，因此它不采用松弛变量或软限制以避免优化失败。我们采用基于优先的目标规划方法来防止僵局而无需额外的程序来确定要屈服的机器人。所提出的算法可以平均将60个代理的轨迹平均每代理使用英特尔I7笔记本电脑计算60个代理，并与基于软限制的基线相比，显示了类似的飞行距离和距离。我们核实所提出的方法可以在随机森林和室内空间中没有僵局达到目标，并且我们通过在迷宫状环境中使用10个时段的真正飞行试验验证了所提出的算法的安全性和可操作性。

ICECUBE是一种用于检测1 GEV和1 PEV之间大气和天体中微子的光学传感器的立方公斤阵列，该阵列已部署1.45 km至2.45 km的南极的冰盖表面以下1.45 km至2.45 km。来自ICE探测器的事件的分类和重建在ICeCube数据分析中起着核心作用。重建和分类事件是一个挑战，这是由于探测器的几何形状，不均匀的散射和冰中光的吸收，并且低于100 GEV的光，每个事件产生的信号光子数量相对较少。为了应对这一挑战，可以将ICECUBE事件表示为点云图形，并将图形神经网络（GNN）作为分类和重建方法。 GNN能够将中微子事件与宇宙射线背景区分开，对不同的中微子事件类型进行分类，并重建沉积的能量，方向和相互作用顶点。基于仿真，我们提供了1-100 GEV能量范围的比较与当前ICECUBE分析中使用的当前最新最大似然技术，包括已知系统不确定性的影响。对于中微子事件分类，与当前的IceCube方法相比，GNN以固定的假阳性速率（FPR）提高了信号效率的18％。另外，GNN在固定信号效率下将FPR的降低超过8（低于半百分比）。对于能源，方向和相互作用顶点的重建，与当前最大似然技术相比，分辨率平均提高了13％-20％。当在GPU上运行时，GNN能够以几乎是2.7 kHz的中位数ICECUBE触发速率的速率处理ICECUBE事件，这打开了在在线搜索瞬态事件中使用低能量中微子的可能性。

对比学习的核心思想是区分不同的实例，并从相同实例中强制不同的视图以共享相同的表示。为了避免琐碎的解决方案，增强在生成不同视图中起重要作用，其中显示了随机裁剪来对模型来学习广义和鲁棒的表示。常用的随机作物操作保持沿着训练过程不变的两个视图之间的分布。在这项工作中，我们表明，自适应地控制沿着训练过程的两个增强视图之间的视差增强了学习的表示的质量。具体而言，我们提出了一种参数立方裁剪操作，用于视频对比度学习，其通过可分辨率的3D仿射变换自动批量3D立方。参数使用对抗目标与视频骨干同时培训，并从数据中学习最佳裁剪策略。可视化表明，参数自适应地控制了两个增强视图之间的中心距离和IOU，并且沿着训练过程的差异中的学习变化是有利于学习强烈的表示。广泛的消融研究证明了所提出的参数对多个对比学习框架和视频骨干的有效性。可以使用代码和模型。

Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have in turn led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgency to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. In this paper, we present our work on integrating multiple data sources in SmokeyNet, a deep learning model using spatio-temporal information to detect smoke from wildland fires. Camera image data is integrated with weather sensor measurements and processed by SmokeyNet to create a multimodal wildland fire smoke detection system. We present our results comparing performance in terms of both accuracy and time-to-detection for multimodal data vs. a single data source. With a time-to-detection of only a few minutes, SmokeyNet can serve as an automated early notification system, providing a useful tool in the fight against destructive wildfires.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

While 3D GANs have recently demonstrated the high-quality synthesis of multi-view consistent images and 3D shapes, they are mainly restricted to photo-realistic human portraits. This paper aims to extend 3D GANs to a different, but meaningful visual form: artistic portrait drawings. However, extending existing 3D GANs to drawings is challenging due to the inevitable geometric ambiguity present in drawings. To tackle this, we present Dr.3D, a novel adaptation approach that adapts an existing 3D GAN to artistic drawings. Dr.3D is equipped with three novel components to handle the geometric ambiguity: a deformation-aware 3D synthesis network, an alternating adaptation of pose estimation and image synthesis, and geometric priors. Experiments show that our approach can successfully adapt 3D GANs to drawings and enable multi-view consistent semantic editing of drawings.