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.

Video recognition in an open and dynamic world is quite challenging, as we need to handle different settings such as close-set, long-tail, few-shot and open-set. By leveraging semantic knowledge from noisy text descriptions crawled from the Internet, we focus on the general video recognition (GVR) problem of solving different recognition tasks within a unified framework. The core contribution of this paper is twofold. First, we build a comprehensive video recognition benchmark of Kinetics-GVR, including four sub-task datasets to cover the mentioned settings. To facilitate the research of GVR, we propose to utilize external textual knowledge from the Internet and provide multi-source text descriptions for all action classes. Second, inspired by the flexibility of language representation, we present a unified visual-linguistic framework (VLG) to solve the problem of GVR by an effective two-stage training paradigm. Our VLG is first pre-trained on video and language datasets to learn a shared feature space, and then devises a flexible bi-modal attention head to collaborate high-level semantic concepts under different settings. Extensive results show that our VLG obtains the state-of-the-art performance under four settings. The superior performance demonstrates the effectiveness and generalization ability of our proposed framework. We hope our work makes a step towards the general video recognition and could serve as a baseline for future research. The code and models will be available at https://github.com/MCG-NJU/VLG.

在本文中，我们提出了一个名为OcSampler的框架，以探索一个紧凑而有效的视频表示，其中一个短剪辑以获得高效的视频识别。最近的作品宁愿通过根据其重要性选择一个框架作为顺序决策任务的帧采样，而我们呈现了一个专用的学习实例的视频冷凝策略的新范式，以选择仅在单个视频中表示整个视频的信息帧步。我们的基本动机是高效的视频识别任务在于一次地处理整个序列而不是顺序拾取帧。因此，这些策略在一个步骤中与简单而有效的策略网络一起导出从光加权略微脱脂网络。此外，我们以帧编号预算扩展了所提出的方法，使框架能够以尽可能少的帧的高度置信度产生正确的预测。四个基准测试，即ActivityNet，Mini-Kinetics，FCVID，Mini-Sports1M的实验证明了我们在准确性，理论计算费用，实际推理速度方面对先前方法的效果。我们还在不同分类器，采样框架和搜索空间上评估其泛化电量。特别是，我们在ActivityNet上达到76.9％的地图和21.7 GFLOPS，具有令人印象深刻的吞吐量：123.9个视频/ s在单个Titan XP GPU上。

High-definition (HD) semantic map generation of the environment is an essential component of autonomous driving. Existing methods have achieved good performance in this task by fusing different sensor modalities, such as LiDAR and camera. However, current works are based on raw data or network feature-level fusion and only consider short-range HD map generation, limiting their deployment to realistic autonomous driving applications. In this paper, we focus on the task of building the HD maps in both short ranges, i.e., within 30 m, and also predicting long-range HD maps up to 90 m, which is required by downstream path planning and control tasks to improve the smoothness and safety of autonomous driving. To this end, we propose a novel network named SuperFusion, exploiting the fusion of LiDAR and camera data at multiple levels. We benchmark our SuperFusion on the nuScenes dataset and a self-recorded dataset and show that it outperforms the state-of-the-art baseline methods with large margins. Furthermore, we propose a new metric to evaluate the long-range HD map prediction and apply the generated HD map to a downstream path planning task. The results show that by using the long-range HD maps predicted by our method, we can make better path planning for autonomous vehicles. The code will be available at https://github.com/haomo-ai/SuperFusion.

In massive multiple-input multiple-output (MIMO) systems, the user equipment (UE) needs to feed the channel state information (CSI) back to the base station (BS) for the following beamforming. But the large scale of antennas in massive MIMO systems causes huge feedback overhead. Deep learning (DL) based methods can compress the CSI at the UE and recover it at the BS, which reduces the feedback cost significantly. But the compressed CSI must be quantized into bit streams for transmission. In this paper, we propose an adaptor-assisted quantization strategy for bit-level DL-based CSI feedback. First, we design a network-aided adaptor and an advanced training scheme to adaptively improve the quantization and reconstruction accuracy. Moreover, for easy practical employment, we introduce the expert knowledge of data distribution and propose a pluggable and cost-free adaptor scheme. Experiments show that compared with the state-of-the-art feedback quantization method, this adaptor-aided quantization strategy can achieve better quantization accuracy and reconstruction performance with less or no additional cost. The open-source codes are available at https://github.com/zhangxd18/QCRNet.

在各个领域（例如政治，健康和娱乐）中的真实和虚假新闻每天都通过在线社交媒体传播，需要对多个领域进行虚假新闻检测。其中，在政治和健康等特定领域中的虚假新闻对现实世界产生了更严重的潜在负面影响（例如，由Covid-19的错误信息引导的流行病）。先前的研究着重于多域假新闻检测，同样采矿和建模域之间的相关性。但是，这些多域方法遇到了SEESAW问题：某些域的性能通常会以损害其他域的性能而改善，这可能导致在特定领域的表现不满意。为了解决这个问题，我们建议一个用于假新闻检测（DITFEND）的域和实例级传输框架，这可以改善特定目标域的性能。为了传递粗粒域级知识，我们从元学习的角度训练了所有域数据的通用模型。为了传输细粒度的实例级知识并将一般模型调整到目标域，我们在目标域上训练语言模型，以评估每个数据实例在源域中的可传递性，并重新赢得每个实例的贡献。两个数据集上的离线实验证明了Ditfend的有效性。在线实验表明，在现实世界中，Ditfend对基本模型带来了更多改进。

多文件科学摘要（MDSS）旨在为与主题相关的科学论文群生成连贯和简洁的摘要。此任务需要精确理解纸张内容以及对交叉纸关系的准确建模。知识图为文档传达了紧凑且可解释的结构化信息，这使其非常适合内容建模和关系建模。在本文中，我们提出了KGSUM，这是一个MDSS模型，以编码和解码过程中的知识图为中心。具体而言，在编码过程中，提出了两个基于图的模块，以将知识图信息纳入纸张编码，而在解码过程中，我们通过以描述性句子的形式首先生成摘要的知识图，提出了一个两阶段解码器。 ，然后生成最终摘要。经验结果表明，所提出的体系结构对多XSCIENCE数据集的基准进行了实质性改进。

人类运动转移是指合成的照片现实和时间连贯的视频，使一个人能够模仿他人的运动。但是，当前的合成视频遭受了序列帧的时间不一致，这些框架显着降低了视频质量，但远未通过像素域中的现有方法来解决。最近，由于图像合成方法的频率不足，一些有关DeepFake检测的作品试图区分频域中的自然图像和合成图像。尽管如此，从自然和合成视频之间的频域间隙方面的各个方面研究合成视频的时间不一致。在本文中，我们建议深入研究频率空间，以进行时间一致的人类运动转移。首先，我们对频域中的自然和合成视频进行了首次综合分析，以揭示单个帧的空间维度和视频的时间维度的频率差距。为了弥补自然视频和合成视频之间的频率差距，我们提出了一个新型的基于频率的人类运动转移框架，名为Fremotr，该框架可以有效地减轻空间伪像以及合成视频的时间不一致。 Fremotr探索了两个基于频率的新型正则化模块：1）频域外观正则化（FAR），以改善个人在单个帧中的外观和2）时间频率正则化（TFR），以确保相邻框架之间的时间一致性。最后，全面的实验表明，FremoTR不仅在时间一致性指标中产生卓越的性能，而且还提高了合成视频的框架级视觉质量。特别是，时间一致性指标比最新模型提高了近30％。

训练深神经网络（DNNS）是机器学习中的一个重要且具有挑战性的优化问题，由于其非凸度和不可分割的结构。交替的最小化方法（AM）方法分割了DNN的组成结构，并引起了深度学习和优化社区的极大兴趣。在本文中，我们提出了一个统一的框架，用于分析AM型网络培训方法的收敛速率。我们的分析基于$ j $ step的足够减少条件和Kurdyka-lojasiewicz（KL）属性，该属性放松了设计下降算法的要求。如果KL Exponent $ \ theta $在$ [0,1）$方面显示详细的本地收敛率。此外，在更强大的$ j $步骤中讨论了本地R线性收敛。

准确的移动对象细分是自动驾驶的重要任务。它可以为许多下游任务提供有效的信息，例如避免碰撞，路径计划和静态地图构建。如何有效利用时空信息是3D激光雷达移动对象分割（LIDAR-MOS）的关键问题。在这项工作中，我们提出了一个新型的深神经网络，利用了时空信息和不同的LiDAR扫描表示方式，以提高LIDAR-MOS性能。具体而言，我们首先使用基于图像图像的双分支结构来分别处理可以从顺序的LiDAR扫描获得的空间和时间信息，然后使用运动引导的注意模块组合它们。我们还通过3D稀疏卷积使用点完善模块来融合LIDAR范围图像和点云表示的信息，并减少对象边界上的伪像。我们验证了我们提出的方法对Semantickitti的LiDAR-MOS基准的有效性。我们的方法在LiDar-Mos IOU方面大大优于最先进的方法。从设计的粗到精细体系结构中受益，我们的方法以传感器框架速率在线运行。我们方法的实现可作为开源可用：https：//github.com/haomo-ai/motionseg3d。