Recent studies have shown that using an external Language Model (LM) benefits the end-to-end Automatic Speech Recognition (ASR). However, predicting tokens that appear less frequently in the training set is still quite challenging. The long-tail prediction problems have been widely studied in many applications, but only been addressed by a few studies for ASR and LMs. In this paper, we propose a new memory augmented lookup dictionary based Transformer architecture for LM. The newly introduced lookup dictionary incorporates rich contextual information in training set, which is vital to correctly predict long-tail tokens. With intensive experiments on Chinese and English data sets, our proposed method is proved to outperform the baseline Transformer LM by a great margin on both word/character error rate and tail tokens error rate. This is achieved without impact on the decoding efficiency. Overall, we demonstrate the effectiveness of our proposed method in boosting the ASR decoding performance, especially for long-tail tokens.

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.

Air pollution is a crucial issue affecting human health and livelihoods, as well as one of the barriers to economic and social growth. Forecasting air quality has become an increasingly important endeavor with significant social impacts, especially in emerging countries like China. In this paper, we present a novel Transformer architecture termed AirFormer to collectively predict nationwide air quality in China, with an unprecedented fine spatial granularity covering thousands of locations. AirFormer decouples the learning process into two stages -- 1) a bottom-up deterministic stage that contains two new types of self-attention mechanisms to efficiently learn spatio-temporal representations; 2) a top-down stochastic stage with latent variables to capture the intrinsic uncertainty of air quality data. We evaluate AirFormer with 4-year data from 1,085 stations in the Chinese Mainland. Compared to the state-of-the-art model, AirFormer reduces prediction errors by 5%~8% on 72-hour future predictions. Our source code is available at https://github.com/yoshall/airformer.

在本文中，我们证明了基于深度学习的方法可用于融合多对象密度。给定一个带有几个传感器可能不同视野的传感器的方案，跟踪器在每个传感器中在本地执行跟踪，该跟踪器会产生随机有限的集合多对象密度。为了融合来自不同跟踪器的输出，我们调整了最近提出的基于变压器的多对象跟踪器，其中融合结果是一个全局的多对象密度，描述了当前时间的所有活物体。我们将基于变压器的融合方法与基于模型的贝叶斯融合方法的性能进行比较，在几种模拟方案中，使用合成数据进行了不同的参数设置。仿真结果表明，基于变压器的融合方法在我们的实验场景中优于基于模型的贝叶斯方法。

本文提供了在标记的随机有限套装文献中使用的自适应出生模型与带有点目标模型的Poisson Multi-Bernoulli混合物（PMBM）滤波器中使用的轨道启动之间的比较分析。 PMBM轨道启动是通过对预测的PMBM密度应用的贝叶斯规则获得的，并为每个接收的测量值创建一个Bernoulli组件，表示该测量可能是混乱或从新目标中检测的。自适应出生模拟此过程，通过使用不同的规则为每个测量创建一个伯努利组件来确定存在的概率和用户定义的单目标密度。本文首先对基于孤立测量的轨道启动中产生的差异进行了分析。然后，它表明自适应出生低估了共同建模假设下监视区域中存在的对象数量。最后，我们提供数值模拟，以进一步说明差异。

对于许多在线平台（例如，视频共享网站，电子商务系统），学习动态用户的偏好已成为越来越重要的组成部分，以提出顺序建议。先前的工作已经做出了许多努力，以基于各种体系结构（例如，经常性的神经网络和自我注意机制）对用户交互序列进行建模项目项目过渡。最近出现的图形神经网络还用作有用的骨干模型，可在顺序推荐方案中捕获项目依赖性。尽管它们有效，但现有的方法却远远集中在具有单一相互作用类型的项目序列表示上，因此仅限于捕获用户和项目之间的动态异质关系结构（例如，页面视图，添加最佳选择，购买，购买）。为了应对这一挑战，我们设计了多行为超毛力增强的变压器框架（MBHT），以捕获短期和长期跨型行为依赖性。具体而言，多尺度变压器配备了低级别的自我注意力，可从细粒度和粗粒水平的共同编码行为感知的顺序模式。此外，我们将全局多行为依赖性纳入HyperGraph神经体系结构中，以自定义的方式捕获层次长期项目相关性。实验结果证明了我们MBHT在不同环境中的各种最新推荐解决方案的优势。进一步的消融研究证明了我们的模型设计和新MBHT框架的好处的有效性。我们的实施代码在以下网址发布：https：//github.com/yuh-yang/mbht-kdd22。

多对象跟踪（MOT）是现代高级驾驶员辅助系统（ADA）和自动驾驶（AD）系统的关键应用之一。 MOT的大多数解决方案都是基于随机矢量贝叶斯过滤器，例如Global最近的邻居（GNN）以及基于规则的启发轨道维护。随着随机有限集（RFS）理论的发展，最近已将RFS贝叶斯过滤器应用于ADA和AD Systems的MOT任务中。但是，由于计算成本和实施复杂性，它们在实际流量中的有用性是对疑问的。在本文中，据透露，具有基于规则的启发式轨道维护的GNN不足以在ADA和AD系统中基于激光雷达的MOT任务。通过系统地比较几个不同的基于对象过滤器的跟踪框架，包括传统的随机矢量贝叶斯滤波器，以及基于规则的启发式跟踪维护和RFS贝叶斯过滤器，可以说明这种判断。此外，提出了一个简单有效的跟踪器，即使用全局最近邻居（GNN-PMB）跟踪器的Poisson Multi-Bernoulli滤波器，建议用于基于激光雷达的MOT任务。拟议的GNN-PMB跟踪器在Nuscenes测试数据集中取得了竞争性的结果，并显示出优于其他最先进的LIDAR的跟踪性能，而Haver Holly Holling Trackers，Lidar和基于摄像机的基于摄像头的跟踪器。

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of <instrument, verb, target> combination delivers comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms by competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery.

汽车MMWAVE雷达在高级驾驶员辅助系统（ADA）和自动驾驶中起关键作用。基于深度学习的实例细分可以从雷达检测点实时对象识别。在常规培训过程中，准确的注释是关键。然而，由于雷达检测点的高质量注释，由于其歧义和稀疏性，要实现挑战。为了解决这个问题，我们提出了一种实施基于雷达检测点的实例细分的对比学习方法。我们根据地面真相标签定义正面和负样品，将对比度损失首先训练模型，然后对以下下游任务进行微调。此外，可以将这两个步骤合并为一个，并且可以为未标记的数据生成伪标签，以进一步提高性能。因此，我们的方法有四种不同的培训设置。实验表明，当仅适用于一小部分培训数据时，我们的方法仍然可以与以100％基真实信息进行监督的方式实现可比的性能。

现代智能手机可以在60〜Hz中持续流动多百万像素RGB图像，与高质量的3D姿势信息和低分辨率LIDAR驱动深度估计同步。在快照照片期间，摄影师的手的自然不稳定性提供了相机姿势的毫米级别变化，我们可以在圆形缓冲器中与RGB和深度一起捕获。在这项工作中，我们探索如何从取景期间获得的这些测量束，我们可以将密集的微基线线视差提示与千克激光雷达深度相结合，以蒸馏高保真深度图。我们采取测试时间优化方法并训练坐标MLP，以沿着摄影师的自然抖动跟踪的路径的连续坐标输出光度计和几何一致深度估计。该方法将高分辨率深度估计为“点拍摄”桌面摄影而言，不需要额外的硬件，人造手动运动或超出按钮的按钮的用户交互。