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.

在过去的十年中，AI AID毒品发现（AIDD）的计算方法和数据集策划的繁荣发展。但是，现实世界中的药物数据集经常表现出高度不平衡的分布，这在很大程度上被当前的文献忽略了，但可能会严重损害机器学习应用程序的公平性和概括。在这一观察结果的激励下，我们介绍了Imdrug，这是一个全面的基准标准，其开源python库由4个不平衡设置，11个AI-Ready数据集，54个学习任务和16种为不平衡学习量身定制的基线算法。它为涵盖广泛的药物发现管道（例如分子建模，药物靶标相互作用和逆合合成）的问题和解决方案提供了可访问且可定制的测试床。我们通过新的评估指标进行广泛的实证研究，以证明现有算法在数据不平衡情况下无法解决药物和药物挑战。我们认为，Imdrug为未来的研究和发展开辟了途径，在AIDD和深度不平衡学习的交集中对现实世界中的挑战开辟了道路。

低光图像噪声和色差的问题是对象检测，语义分割，实例分割等任务的挑战性问题。在本文中，我们提出了用于低照明增强的算法。KIND-LE使用网络结构中的光曲线估计模块来增强视网膜分解图像中的照明图，从而改善图像亮度。我们提出了照明图和反射图融合模块，以恢复恢复的图像细节并减少细节损失。最后，我们包括了消除噪声的总变化损失函数。我们的方法将GLADNET数据集用作训练集，而LOL数据集则是测试集，并使用Exdark作为下游任务的数据集进行了验证。基准上的广泛实验证明了我们方法的优势，并且接近最先进的结果，该结果的PSNR为19.7216，SSIM在指标方面为0.8213。

密集的视频字幕（DVC）的任务旨在为一个视频中的多个事件制作带有时间戳的字幕。语义信息对于DVC的本地化和描述都起着重要作用。我们提出了基于编码编码框架的语义辅助密集的视频字幕模型。在编码阶段，我们设计了一个概念检测器来提取语义信息，然后将其与多模式的视觉特征融合在一起，以充分代表输入视频。在解码阶段，我们设计了一个与本地化和字幕的分类头，以提供语义监督。我们的方法在DVC评估指标下对Youmakeup数据集进行了重大改进，并在PIC 4TH挑战的化妆密集视频字幕（MDVC）任务中实现了高性能。

我们描述了一种使用机器人应用程序中常见的一类离散连续因子图进行平滑和映射的通用方法。虽然有公开可用的工具提供灵活且易于使用的接口，以指定和解决以离散或连续图形模型提出的优化问题，但目前尚无类似的一般工具，可以为混合离散性问题提供相同的功能。我们旨在解决这个问题。特别是，我们提供了一个库DC-SAM，将现有的工具扩展为以因子图定义的优化问题，以设置离散模型的设置。我们工作的关键贡献是一种新颖的解决方案，用于有效地回收离散连续优化问题的近似解决方案。我们方法的关键见解是，虽然对连续和离散状态空间的共同推断通常很难，但许多通常遇到的离散连续问题自然可以分为“离散部分”，并且可以轻松地解决的“连续部分” 。利用这种结构，我们以交替的方式优化离散和连续变量。因此，我们提出的工作可以直接表示离散图形模型的直接表示和近似推断。我们还提供了一种方法来恢复离散变量和连续变量的估计值的不确定性。我们通过应用于三个不同的机器人感知应用程序的应用来证明我们的方法的多功能性：点云注册，健壮的姿势图优化以及基于对象的映射和本地化。

对象姿势预测的最新进展为机器人在导航期间构建对象级场景表示形式提供了有希望的途径。但是，当我们在新颖环境中部署机器人时，分发数据可能会降低预测性能。为了减轻域间隙，我们可以使用机器人捕获图像作为伪标签的预测在目标域中进行自我训练，以微调对象姿势估计器。不幸的是，姿势预测通常是折磨的，很难量化它们的不确定性，这可能会导致低质量的伪标记数据。为了解决这个问题，我们提出了一种猛烈支持的自我训练方法，利用机器人对3D场景几何形状的理解来增强对象姿势推断性能。将姿势预测与机器人探光仪相结合，我们制定并求解姿势图优化以完善对象姿势估计，并使伪标签在整个帧中更加一致。我们将姿势预测协方差纳入变量中，以自动建模其不确定性。这种自动协方差调整（ACT）过程可以在组件级别拟合6D姿势预测噪声，从而导致高质量的伪训练数据。我们在YCB视频数据集和实际机器人实验中使用深对象姿势估计器（DOPE）测试我们的方法。它在两种测试中的姿势预测中分别达到34.3％和17.8％的精度提高。我们的代码可在https://github.com/520xyxyzq/slam-super-6d上找到。

In this paper we revisit endless online level generation with the recently proposed experience-driven procedural content generation via reinforcement learning (EDRL) framework, from an observation that EDRL tends to generate recurrent patterns. Inspired by this phenomenon, we formulate a notion of state space closure, which means that any state that may appear in an infinite-horizon online generation process can be found in a finite horizon. Through theoretical analysis we find that though state space closure arises a concern about diversity, it makes the EDRL trained on a finite-horizon generalised to the infinite-horizon scenario without deterioration of content quality. Moreover, we verify the quality and diversity of contents generated by EDRL via empirical studies on the widely used Super Mario Bros. benchmark. Experimental results reveal that the current EDRL approach's ability of generating diverse game levels is limited due to the state space closure, whereas it does not suffer from reward deterioration given a horizon longer than the one of training. Concluding our findings and analysis, we argue that future works in generating online diverse and high-quality contents via EDRL should address the issue of diversity on the premise of state space closure which ensures the quality.

Segmenting the fine structure of the mouse brain on magnetic resonance (MR) images is critical for delineating morphological regions, analyzing brain function, and understanding their relationships. Compared to a single MRI modality, multimodal MRI data provide complementary tissue features that can be exploited by deep learning models, resulting in better segmentation results. However, multimodal mouse brain MRI data is often lacking, making automatic segmentation of mouse brain fine structure a very challenging task. To address this issue, it is necessary to fuse multimodal MRI data to produce distinguished contrasts in different brain structures. Hence, we propose a novel disentangled and contrastive GAN-based framework, named MouseGAN++, to synthesize multiple MR modalities from single ones in a structure-preserving manner, thus improving the segmentation performance by imputing missing modalities and multi-modality fusion. Our results demonstrate that the translation performance of our method outperforms the state-of-the-art methods. Using the subsequently learned modality-invariant information as well as the modality-translated images, MouseGAN++ can segment fine brain structures with averaged dice coefficients of 90.0% (T2w) and 87.9% (T1w), respectively, achieving around +10% performance improvement compared to the state-of-the-art algorithms. Our results demonstrate that MouseGAN++, as a simultaneous image synthesis and segmentation method, can be used to fuse cross-modality information in an unpaired manner and yield more robust performance in the absence of multimodal data. We release our method as a mouse brain structural segmentation tool for free academic usage at https://github.com/yu02019.

Adversarial attacks can easily fool object recognition systems based on deep neural networks (DNNs). Although many defense methods have been proposed in recent years, most of them can still be adaptively evaded. One reason for the weak adversarial robustness may be that DNNs are only supervised by category labels and do not have part-based inductive bias like the recognition process of humans. Inspired by a well-known theory in cognitive psychology -- recognition-by-components, we propose a novel object recognition model ROCK (Recognizing Object by Components with human prior Knowledge). It first segments parts of objects from images, then scores part segmentation results with predefined human prior knowledge, and finally outputs prediction based on the scores. The first stage of ROCK corresponds to the process of decomposing objects into parts in human vision. The second stage corresponds to the decision process of the human brain. ROCK shows better robustness than classical recognition models across various attack settings. These results encourage researchers to rethink the rationality of currently widely-used DNN-based object recognition models and explore the potential of part-based models, once important but recently ignored, for improving robustness.

Neural networks are susceptible to data inference attacks such as the membership inference attack, the adversarial model inversion attack and the attribute inference attack, where the attacker could infer useful information such as the membership, the reconstruction or the sensitive attributes of a data sample from the confidence scores predicted by the target classifier. In this paper, we propose a method, namely PURIFIER, to defend against membership inference attacks. It transforms the confidence score vectors predicted by the target classifier and makes purified confidence scores indistinguishable in individual shape, statistical distribution and prediction label between members and non-members. The experimental results show that PURIFIER helps defend membership inference attacks with high effectiveness and efficiency, outperforming previous defense methods, and also incurs negligible utility loss. Besides, our further experiments show that PURIFIER is also effective in defending adversarial model inversion attacks and attribute inference attacks. For example, the inversion error is raised about 4+ times on the Facescrub530 classifier, and the attribute inference accuracy drops significantly when PURIFIER is deployed in our experiment.