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.

Pedestrian detection in the wild remains a challenging problem especially for scenes containing serious occlusion. In this paper, we propose a novel feature learning method in the deep learning framework, referred to as Feature Calibration Network (FC-Net), to adaptively detect pedestrians under various occlusions. FC-Net is based on the observation that the visible parts of pedestrians are selective and decisive for detection, and is implemented as a self-paced feature learning framework with a self-activation (SA) module and a feature calibration (FC) module. In a new self-activated manner, FC-Net learns features which highlight the visible parts and suppress the occluded parts of pedestrians. The SA module estimates pedestrian activation maps by reusing classifier weights, without any additional parameter involved, therefore resulting in an extremely parsimony model to reinforce the semantics of features, while the FC module calibrates the convolutional features for adaptive pedestrian representation in both pixel-wise and region-based ways. Experiments on CityPersons and Caltech datasets demonstrate that FC-Net improves detection performance on occluded pedestrians up to 10% while maintaining excellent performance on non-occluded instances.

Ego-pose estimation and dynamic object tracking are two critical problems for autonomous driving systems. The solutions to these problems are generally based on their respective assumptions, \ie{the static world assumption for simultaneous localization and mapping (SLAM) and the accurate ego-pose assumption for object tracking}. However, these assumptions are challenging to hold in dynamic road scenarios, where SLAM and object tracking become closely correlated. Therefore, we propose DL-SLOT, a dynamic LiDAR SLAM and object tracking method, to simultaneously address these two coupled problems. This method integrates the state estimations of both the autonomous vehicle and the stationary and dynamic objects in the environment into a unified optimization framework. First, we used object detection to identify all points belonging to potentially dynamic objects. Subsequently, a LiDAR odometry was conducted using the filtered point cloud. Simultaneously, we proposed a sliding window-based object association method that accurately associates objects according to the historical trajectories of tracked objects. The ego-states and those of the stationary and dynamic objects are integrated into the sliding window-based collaborative graph optimization. The stationary objects are subsequently restored from the potentially dynamic object set. Finally, a global pose-graph is implemented to eliminate the accumulated error. Experiments on KITTI datasets demonstrate that our method achieves better accuracy than SLAM and object tracking baseline methods. This confirms that solving SLAM and object tracking simultaneously is mutually advantageous, dramatically improving the robustness and accuracy of SLAM and object tracking in dynamic road scenarios.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

3D场景由大量背景点主导，这对于主要需要集中在前景对象的检测任务是多余的。在本文中，我们分析了现有的稀疏3D CNN的主要组成部分，发现3D CNN忽略了数据的冗余，并在下降过程中进一步扩大了数据，这带来了大量的多余和不必要的计算间开销。受到这一点的启发，我们提出了一个名为“空间修剪稀疏卷积”（SPS-CONV）的新型卷积操作员，其中包括两个变体，空间修剪的Submanifold稀疏卷积（SPSS-CONV）和空间修剪的常规稀疏卷积（SPRS-CONV），包括这是基于动态确定冗余降低关键领域的想法。我们验证该幅度可以作为确定摆脱基于学习方法的额外计算的关键领域的重要提示。提出的模块可以轻松地将其纳入现有的稀疏3D CNN中，而无需额外的架构修改。关于Kitti，Waymo和Nuscenes数据集的广泛实验表明，我们的方法可以在不损害性能的情况下实现超过50％的GFLOPS。

本文回顾了AIM 2022上压缩图像和视频超级分辨率的挑战。这项挑战包括两条曲目。轨道1的目标是压缩图像的超分辨率，轨迹〜2靶向压缩视频的超分辨率。在轨道1中，我们使用流行的数据集DIV2K作为培训，验证和测试集。在轨道2中，我们提出了LDV 3.0数据集，其中包含365个视频，包括LDV 2.0数据集（335个视频）和30个其他视频。在这一挑战中，有12支球队和2支球队分别提交了赛道1和赛道2的最终结果。所提出的方法和解决方案衡量了压缩图像和视频上超分辨率的最先进。提出的LDV 3.0数据集可在https://github.com/renyang-home/ldv_dataset上找到。此挑战的首页是在https://github.com/renyang-home/aim22_compresssr。

否决单图是一项普遍但又具有挑战性的任务。复杂的降雪降解和各种降解量表需要强大的代表能力。为了使否定的网络看到各种降雪并建模本地细节和全球信息的上下文相互作用，我们提出了一种称为Snowformer的功能强大的建筑。首先，它在编码器中执行比例感知功能聚合，以捕获各种降解的丰富积雪信息。其次，为了解决大规模降级，它使用了解码器中的新颖上下文交互变压器块，该互动器块在全球上下文交互中从前范围内的局部细节和全局信息进行了上下文交互。并引入本地上下文互动可改善场景细节的恢复。第三，我们设计了一个异质的特征投影头，该功能投影头逐渐融合了编码器和解码器的特征，并将精制功能投影到干净的图像中。广泛的实验表明，所提出的雪诺形雪孔比其他SOTA方法取得了重大改进。与SOTA单图像HDCW-NET相比，它在CSD测试集上将PSNR度量提高了9.2dB。此外，与一般图像恢复体系结构NAFNET相比，PSNR的增加5.13db，这验证了我们的雪诺形雪地降雪任务的强大表示能力。该代码在\ url {https://github.com/ephemeral182/snowformer}中发布。

对具有无限宽度的神经网络的研究对于更好地理解实际应用中的神经网络很重要。在这项工作中，我们得出了深，无限宽度的Maxout网络和高斯过程（GP）的等效性，并用组成结构表征Maxout内核。此外，我们建立了深厚的Maxout网络内核与深神经网络内核之间的联系。我们还提供了有效的数值实现，可以适应任何麦克斯特等级。数值结果表明，与有限宽度的对应物和深神经网络内核相比，基于深层Maxout网络内核进行贝叶斯推论可能会导致竞争成果。这使我们启发了麦克斯的激活也可以纳入其他无限宽度神经网络结构，例如卷积神经网络（CNN）。

联合学习（FL）是一种使用跨设备分布的数据训练模型的技术。差异隐私（DP）为敏感数据提供了正式的隐私保证。我们的目标是在使用FL和DP保护隐私的同时，在计算受限设备上训练大型神经网络语言模型（NNLM）。但是，随着模型大小的增长，引入模型的DP噪声增加，这通常会阻止收敛。我们提出了部分嵌入更新（PEU），这是一种新颖的技术，可以通过降低有效载荷大小来降低噪声。此外，我们采用低级适应（LORA）和噪声对比估计（NCE）来减少计算受限设备上大型模型的记忆需求。这种技术的组合使得可以在保留准确性和隐私的同时训练大型唱机语言模型。

评估对象图像的模糊对于提高对象识别和检索的性能至关重要。主要挑战在于缺乏具有可靠标签和有效学习策略的丰富图像。当前的数据集标记为有限且混乱的质量水平。为了克服这一限制，我们建议将成对图像之间的等级关系标记，而不是它们的质量水平，因为人类更容易标记，并建立具有可靠标签的大规模逼真的面部图像模糊评估数据集。基于此数据集，我们提出了一种仅以成对等级标签作为监督的方法来获得模糊分数。此外，为了进一步提高绩效，我们提出了一种基于四倍体排名一致性的自制方法，以更有效地利用未标记的数据。受监督和自我监督的方法构成了最终的半监督学习框架，可以端对端训练。实验结果证明了我们方法的有效性。