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.

Recently, the success of pre-training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework. In this paper, we present TencentPretrain, a toolkit supporting pre-training models of different modalities. The core feature of TencentPretrain is the modular design. The toolkit uniformly divides pre-training models into 5 components: embedding, encoder, target embedding, decoder, and target. As almost all of common modules are provided in each component, users can choose the desired modules from different components to build a complete pre-training model. The modular design enables users to efficiently reproduce existing pre-training models or build brand-new one. We test the toolkit on text, vision, and audio benchmarks and show that it can match the performance of the original implementations.

We present a strong object detector with encoder-decoder pretraining and finetuning. Our method, called Group DETR v2, is built upon a vision transformer encoder ViT-Huge~\cite{dosovitskiy2020image}, a DETR variant DINO~\cite{zhang2022dino}, and an efficient DETR training method Group DETR~\cite{chen2022group}. The training process consists of self-supervised pretraining and finetuning a ViT-Huge encoder on ImageNet-1K, pretraining the detector on Object365, and finally finetuning it on COCO. Group DETR v2 achieves $\textbf{64.5}$ mAP on COCO test-dev, and establishes a new SoTA on the COCO leaderboard https://paperswithcode.com/sota/object-detection-on-coco

Myocardial pathology segmentation (MyoPS) can be a prerequisite for the accurate diagnosis and treatment planning of myocardial infarction. However, achieving this segmentation is challenging, mainly due to the inadequate and indistinct information from an image. In this work, we develop an end-to-end deep neural network, referred to as MyoPS-Net, to flexibly combine five-sequence cardiac magnetic resonance (CMR) images for MyoPS. To extract precise and adequate information, we design an effective yet flexible architecture to extract and fuse cross-modal features. This architecture can tackle different numbers of CMR images and complex combinations of modalities, with output branches targeting specific pathologies. To impose anatomical knowledge on the segmentation results, we first propose a module to regularize myocardium consistency and localize the pathologies, and then introduce an inclusiveness loss to utilize relations between myocardial scars and edema. We evaluated the proposed MyoPS-Net on two datasets, i.e., a private one consisting of 50 paired multi-sequence CMR images and a public one from MICCAI2020 MyoPS Challenge. Experimental results showed that MyoPS-Net could achieve state-of-the-art performance in various scenarios. Note that in practical clinics, the subjects may not have full sequences, such as missing LGE CMR or mapping CMR scans. We therefore conducted extensive experiments to investigate the performance of the proposed method in dealing with such complex combinations of different CMR sequences. Results proved the superiority and generalizability of MyoPS-Net, and more importantly, indicated a practical clinical application.

知识蒸馏（KD）可以有效地将知识从繁琐的网络（教师）转移到紧凑的网络（学生），在某些计算机视觉应用中证明了其优势。知识的表示对于知识转移和学生学习至关重要，这通常以手工制作的方式定义或直接使用中间功能。在本文中，我们建议在教师学生架构下为单像超级分辨率任务提出一种模型 - 不足的元知识蒸馏方法。它提供了一种更灵活，更准确的方法，可以通过知识代表网络（KRNET）的能力来帮助教师通过具有可学习参数的知识传输知识。为了提高知识表示对学生需求的看法能力，我们建议通过采用学生特征以及KRNET中的教师和学生之间的相关性来解决从中间产出到转移知识的转型过程。具体而言，生成纹理感知的动态内核，然后提取要改进的纹理特征，并将相应的教师指导分解为质地监督，以进一步促进高频细节的恢复质量。此外，KRNET以元学习方式进行了优化，以确保知识转移和学生学习有益于提高学生的重建质量。在各种单个图像超分辨率数据集上进行的实验表明，我们所提出的方法优于现有的定义知识表示相关的蒸馏方法，并且可以帮助超分辨率算法实现更好的重建质量，而无需引入任何推理复杂性。

最近，社区对模型缩放的关注越来越多，并有助于开发具有广泛尺度的模型家族。当前的方法要么简单地采用单发NAS的方式来构建非结构性和不可缩放的模型家族，要么依靠手动固定的缩放策略来扩展不必要的最佳基础模型。在本文中，我们桥接了两个组件，并将Scalenet提出到共同搜索基础模型和缩放策略，以便缩放大型模型可以具有更有希望的性能。具体来说，我们设计了一个超级植物，以体现具有不同尺寸频谱（例如拖鞋）的模型。然后，可以通过基于马尔可夫链的进化算法与基本模型进行交互学习缩放策略，并概括以开发更大的模型。为了获得一个体面的超级植物，我们设计了一种分层抽样策略，以增强其训练充足并减轻干扰。实验结果表明，我们的缩放网络在各种失败的方面都具有显着的性能优势，但搜索成本至少降低了2.53倍。代码可在https://github.com/luminolx/scalenet上找到。

由于缺乏电感偏见，视觉变压器（VIT）通常被认为比卷积神经网络（CNN）少。因此，最近的工作将卷积作为插件模块，并将其嵌入各种Vit对应物中。在本文中，我们认为卷积内核执行信息聚合以连接所有令牌。但是，如果这种明确的聚合能够以更均匀的方式起作用，则实际上是轻重量VIT的不必要的。受到这一点的启发，我们将Lightvit作为新的轻巧VIT家族，以在不卷积的情况下在纯变压器块上实现更好的准确性效率平衡。具体而言，我们将一个全球但有效的聚合方案引入了VIT的自我注意力和前馈网络（FFN），其中引入了其他可学习的令牌以捕获全球依赖性；在令牌嵌入上施加了双维通道和空间注意力。实验表明，我们的模型在图像分类，对象检测和语义分割任务上取得了重大改进。例如，我们的LightVit-T仅使用0.7G拖鞋的ImageNet上达到78.7％的精度，在GPU上的PVTV2-B0优于8.2％，而GPU的速度快11％。代码可在https://github.com/hunto/lightvit上找到。

在本文中，我们提出了端到端的水疗形式，以从单个阴影图像中恢复无阴影的图像。与需要两个步骤进行阴影检测然后再删除阴影的传统方法不同，Spa-Former将这些步骤统一为一个，这是一个单阶段网络，能够直接学习阴影和无阴影之间的映射功能，不需要一个单独的阴影检测。因此，SPA形式适应于实际图像去阴影，以适应投影在不同语义区域上的阴影。SPA形式由变压器层和一系列关节傅立叶变压残留块和两轮关节空间注意力组成。本文中的网络能够在达到非常快速的处理效率的同时处理任务。我们的代码在https://github.com/ zhangbaijin/spatial-transformer-shadow-removal上重新发布

在过去的几十年中，多机构增强学习（MARL）一直在学术界和行业受到广泛关注。 MAL中的基本问题之一是如何全面评估不同的方法。在视频游戏或简单的模拟场景中评估了大多数现有的MAL方法。这些方法在实际情况下，尤其是多机器人系统中的性能仍然未知。本文介绍了一个可扩展的仿真平台，用于多机器人增强学习（MRRL），称为SMART，以满足这一需求。确切地说，智能由两个组成部分组成：1）一个模拟环境，该环境为培训提供了各种复杂的交互场景，以及2）现实世界中的多机器人系统，用于现实的性能评估。此外，SMART提供了代理环境API，这些API是算法实现的插件。为了说明我们平台的实用性，我们就合作驾驶车道变更方案进行了案例研究。在案例研究的基础上，我们总结了MRRL的一些独特挑战，这些挑战很少被考虑。最后，我们为鼓励和增强MRRL研究的仿真环境，相关的基准任务和最先进的基线开放。

Unlike existing knowledge distillation methods focus on the baseline settings, where the teacher models and training strategies are not that strong and competing as state-of-the-art approaches, this paper presents a method dubbed DIST to distill better from a stronger teacher. We empirically find that the discrepancy of predictions between the student and a stronger teacher may tend to be fairly severer. As a result, the exact match of predictions in KL divergence would disturb the training and make existing methods perform poorly. In this paper, we show that simply preserving the relations between the predictions of teacher and student would suffice, and propose a correlation-based loss to capture the intrinsic inter-class relations from the teacher explicitly. Besides, considering that different instances have different semantic similarities to each class, we also extend this relational match to the intra-class level. Our method is simple yet practical, and extensive experiments demonstrate that it adapts well to various architectures, model sizes and training strategies, and can achieve state-of-the-art performance consistently on image classification, object detection, and semantic segmentation tasks. Code is available at: https://github.com/hunto/DIST_KD .