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.

Current outdoor LiDAR-based 3D object detection methods mainly adopt the training-from-scratch paradigm. Unfortunately, this paradigm heavily relies on large-scale labeled data, whose collection can be expensive and time-consuming. Self-supervised pre-training is an effective and desirable way to alleviate this dependence on extensive annotated data. Recently, masked modeling has become a successful self-supervised learning approach for point clouds. However, current works mainly focus on synthetic or indoor datasets. When applied to large-scale and sparse outdoor point clouds, they fail to yield satisfactory results. In this work, we present BEV-MAE, a simple masked autoencoder pre-training framework for 3D object detection on outdoor point clouds. Specifically, we first propose a bird's eye view (BEV) guided masking strategy to guide the 3D encoder learning feature representation in a BEV perspective and avoid complex decoder design during pre-training. Besides, we introduce a learnable point token to maintain a consistent receptive field size of the 3D encoder with fine-tuning for masked point cloud inputs. Finally, based on the property of outdoor point clouds, i.e., the point clouds of distant objects are more sparse, we propose point density prediction to enable the 3D encoder to learn location information, which is essential for object detection. Experimental results show that BEV-MAE achieves new state-of-the-art self-supervised results on both Waymo and nuScenes with diverse 3D object detectors. Furthermore, with only 20% data and 7% training cost during pre-training, BEV-MAE achieves comparable performance with the state-of-the-art method ProposalContrast. The source code and pre-trained models will be made publicly available.

Storytelling and narrative are fundamental to human experience, intertwined with our social and cultural engagement. As such, researchers have long attempted to create systems that can generate stories automatically. In recent years, powered by deep learning and massive data resources, automatic story generation has shown significant advances. However, considerable challenges, like the need for global coherence in generated stories, still hamper generative models from reaching the same storytelling ability as human narrators. To tackle these challenges, many studies seek to inject structured knowledge into the generation process, which is referred to as structure knowledge-enhanced story generation. Incorporating external knowledge can enhance the logical coherence among story events, achieve better knowledge grounding, and alleviate over-generalization and repetition problems in stories. This survey provides the latest and comprehensive review of this research field: (i) we present a systematical taxonomy regarding how existing methods integrate structured knowledge into story generation; (ii) we summarize involved story corpora, structured knowledge datasets, and evaluation metrics; (iii) we give multidimensional insights into the challenges of knowledge-enhanced story generation and cast light on promising directions for future study.

One-shot segmentation of brain tissues is typically a dual-model iterative learning: a registration model (reg-model) warps a carefully-labeled atlas onto unlabeled images to initialize their pseudo masks for training a segmentation model (seg-model); the seg-model revises the pseudo masks to enhance the reg-model for a better warping in the next iteration. However, there is a key weakness in such dual-model iteration that the spatial misalignment inevitably caused by the reg-model could misguide the seg-model, which makes it converge on an inferior segmentation performance eventually. In this paper, we propose a novel image-aligned style transformation to reinforce the dual-model iterative learning for robust one-shot segmentation of brain tissues. Specifically, we first utilize the reg-model to warp the atlas onto an unlabeled image, and then employ the Fourier-based amplitude exchange with perturbation to transplant the style of the unlabeled image into the aligned atlas. This allows the subsequent seg-model to learn on the aligned and style-transferred copies of the atlas instead of unlabeled images, which naturally guarantees the correct spatial correspondence of an image-mask training pair, without sacrificing the diversity of intensity patterns carried by the unlabeled images. Furthermore, we introduce a feature-aware content consistency in addition to the image-level similarity to constrain the reg-model for a promising initialization, which avoids the collapse of image-aligned style transformation in the first iteration. Experimental results on two public datasets demonstrate 1) a competitive segmentation performance of our method compared to the fully-supervised method, and 2) a superior performance over other state-of-the-art with an increase of average Dice by up to 4.67%. The source code is available at: https://github.com/JinxLv/One-shot-segmentation-via-IST.

Existing deep learning based HDRTV reconstruction methods assume one kind of tone mapping operators (TMOs) as the degradation procedure to synthesize SDRTV-HDRTV pairs for supervised training. In this paper, we argue that, although traditional TMOs exploit efficient dynamic range compression priors, they have several drawbacks on modeling the realistic degradation: information over-preservation, color bias and possible artifacts, making the trained reconstruction networks hard to generalize well to real-world cases. To solve this problem, we propose a learning-based data synthesis approach to learn the properties of real-world SDRTVs by integrating several tone mapping priors into both network structures and loss functions. In specific, we design a conditioned two-stream network with prior tone mapping results as a guidance to synthesize SDRTVs by both global and local transformations. To train the data synthesis network, we form a novel self-supervised content loss to constraint different aspects of the synthesized SDRTVs at regions with different brightness distributions and an adversarial loss to emphasize the details to be more realistic. To validate the effectiveness of our approach, we synthesize SDRTV-HDRTV pairs with our method and use them to train several HDRTV reconstruction networks. Then we collect two inference datasets containing both labeled and unlabeled real-world SDRTVs, respectively. Experimental results demonstrate that, the networks trained with our synthesized data generalize significantly better to these two real-world datasets than existing solutions.

图像文本聚类（ITC）的目标是通过整合这些异质样品的多模式的互补和一致信息来找到正确的簇。但是，目前的大多数研究都根据理想的前提分析了ITC，即每种模式中的样本都是完整的。但是，在现实情况下，这种推定并不总是有效的。缺少的数据问题使图像文本特征学习性能退化，并最终会影响ITC任务中的概括能力。尽管已经提出了一系列方法来解决此不完整的图像文本群集问题（IITC），但仍然存在以下问题：1）大多数现有方法几乎不考虑异质特征域之间的明显差距。 2）对于缺少数据，很少保证由现有方法生成的表示形式适合聚类任务。 3）现有方法不利用内部和内部模式的潜在连接。在本文中，我们提出了一个聚类引起的生成不完整的图像文本聚类（CIGIT-C）网络，以应对上述挑战。更具体地说，我们首先使用特定于模态的编码器将原始功能映射到更独特的子空间。通过使用对抗生成网络在另一种模态上产生一种方式，可以彻底探索内部内部和模式之间的潜在连接。最后，我们使用两个KL DiverGence损失更新相应的模态特异性编码器。公共图像文本数据集的实验结果表明，建议的方法优于IITC作业更有效。

随着方法的发展，反转主要分为两个步骤。第一步是图像嵌入，其中编码器或优化过程嵌入图像以获取相应的潜在代码。之后，第二步旨在完善反转和编辑结果，我们将其命名为“结果”。尽管第二步显着提高了忠诚度，但感知和编辑性几乎没有变化，深处取决于第一步中获得的反向潜在代码。因此，一个关键问题是在保留重建保真度的同时获得更好的感知和编辑性的潜在代码。在这项工作中，我们首先指出，这两个特征与合成分布的逆代码的对齐程度（或不对准）有关。然后，我们提出了潜在空间比对反转范式（LSAP），该范式由评估度量和解决方案组成。具体来说，我们引入了归一化样式空间（$ \ Mathcal {s^n} $ space）和$ \ Mathcal {s^n} $ cosine距离（SNCD）以测量反转方法的不对准。由于我们提出的SNCD是可区分的，因此可以在基于编码器和基于优化的嵌入方法中进行优化，以执行均匀的解决方案。在各个域中进行的广泛实验表明，SNCD有效地反映了感知和编辑性，并且我们的对齐范式在两个步骤中都归档了最新的。代码可在https://github.com/caopulan/ganinverter上找到。

不完整的多视图聚类旨在通过使用来自多种模式的数据来增强聚类性能。尽管已经提出了几种研究此问题的方法，但以下缺点仍然存在：1）很难学习潜在的互补性但不使用标签信息而保持一致性的潜在表示； 2）因此，当完整的数据稀缺时，在不完整的数据中未能充分利用不完整数据中的隐藏信息会导致次优群集性能。在本文中，我们提出了与生成对抗网络（CIMIC-GAN）的对比度不完整的多视图图像聚类，该网络使用GAN填充不完整的数据并使用双对比度学习来学习完整和不完整的数据的一致性。更具体地说，考虑到多种方式之间的多样性和互补信息，我们将完整和不完整数据的自动编码表示为双对比度学习，以实现学习一致性。将gan集成到自动编码过程中不仅可以充分利用不完整数据的新功能，而且可以在存在高数据缺失率的情况下更好地概括该模型。在\ textColor {black} {四}广泛使用的数据集上进行的实验表明，cimic-gan优于最先进的不完整的多视图聚类方法。

3D多对象跟踪（MOT）确保在连续动态检测过程中保持一致性，有利于自动驾驶中随后的运动计划和导航任务。但是，基于摄像头的方法在闭塞情况下受到影响，准确跟踪基于激光雷达的方法的对象的不规则运动可能是具有挑战性的。某些融合方法效果很好，但不认为在遮挡下出现外观特征的不可信问题。同时，错误检测问题也显着影响跟踪。因此，我们根据组合的外观运动优化（Camo-Mot）提出了一种新颖的相机融合3D MOT框架，该框架使用相机和激光镜数据，并大大减少了由遮挡和错误检测引起的跟踪故障。对于遮挡问题，我们是第一个提出遮挡头来有效地选择最佳对象外观的人，从而减少了闭塞的影响。为了减少错误检测在跟踪中的影响，我们根据置信得分设计一个运动成本矩阵，从而提高了3D空间中的定位和对象预测准确性。由于现有的多目标跟踪方法仅考虑一个类别，因此我们还建议建立多类损失，以在多类别场景中实现多目标跟踪。在Kitti和Nuscenes跟踪基准测试上进行了一系列验证实验。我们提出的方法在KITTI测试数据集上的所有多模式MOT方法中实现了最先进的性能和最低的身份开关（IDS）值（CAR为23，行人为137）。并且我们提出的方法在Nuscenes测试数据集上以75.3％的AMOTA进行了所有算法中的最新性能。

荧光镜检查是一种使用X射线来获得3D对象内部的实时2D视频，帮助外科医生观察病理结构和组织功能，尤其是在干预过程中。然而，它主要是由于低剂量X射线的临床使用而产生的，因此需要荧光镜检查技术。这种脱牙受到了成像对象与X射线成像系统之间的相对运动的挑战。我们通过提出一个自制的三阶段框架来应对这一挑战，从而利用荧光镜检查的领域知识。 （i）稳定：我们首先基于光流计算构建动态全景，以稳定X射线检测器的运动引起的非平稳背景。 （ii）分解：然后，我们提出了一种新型的基于掩模的鲁棒原理分析（RPCA）分解方法，以将探测器运动的视频分离为低级别背景和稀疏前景。这样的分解可容纳专家的阅读习惯。 （iii）denoise：我们终于通过自我监督的学习策略分别降低了背景和前景，并通过双侧时空滤波器将deno的部分融合到最终输出中。为了评估我们工作的有效性，我们策划了27个视频（1,568帧）和相应的地面真相的专用荧光镜数据集。我们的实验表明，与标准方法相比，它在降解和增强效果方面取得了重大改进。最后，专家评级确认了这种功效。