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.

整合多个在线社交网络（OSN）对许多下游社交挖掘任务（例如用户偏好建模，建议和链接预测）具有重要意义。但是，不幸的是，伴随着越来越多的隐私问题，泄漏敏感用户信息。如何完全利用来自不同在线社交网络的数据，同时保存用户隐私仍然无法解决。为此，我们提出了一个跨网络的社交用户嵌入框架，即DP-Crosue，以一种隐私性的方式学习用户的全面表示。我们共同考虑具有不同隐私保证的部分调整社交网络的信息。特别是，对于每个异质社交网络，我们首先引入一个混合差异隐私概念，以捕获异构数据类型的隐私期望的变化。接下来，为了找到跨社交网络的用户链接，我们进行了无监督的基于用户嵌入的对齐方式，其中通过异质网络嵌入技术实现了用户嵌入。为了进一步增强用户嵌入，一种新颖的跨网络GCN嵌入模型旨在通过那些对齐用户跨网络传输知识。在三个现实世界数据集上进行的广泛实验表明，我们的方法对用户兴趣预测任务以及捍卫用户属性推理攻击的嵌入进行了重大改进。

对看不见的环境变化的深入强化学习的概括通常需要对大量各种培训变化进行政策学习。我们从经验上观察到，接受过许多变化的代理商（通才）倾向于在一开始就更快地学习，但是长期以来其最佳水平的性能高原。相比之下，只接受一些变体培训的代理商（专家）通常可以在有限的计算预算下获得高回报。为了两全其美，我们提出了一个新颖的通才特权训练框架。具体来说，我们首先培训一名通才的所有环境变化。当它无法改善时，我们会推出大量的专家，并从通才克隆过重量，每个人都接受了训练，以掌握选定的一小部分变化子集。我们终于通过所有专家的示范引起的辅助奖励恢复了通才的培训。特别是，我们调查了开始专业培训的时机，并在专家的帮助下比较策略以学习通才。我们表明，该框架将政策学习的信封推向了包括Procgen，Meta-World和Maniskill在内的几个具有挑战性和流行的基准。

最近，通过协作推断部署深神经网络（DNN）模型，该推断将预训练的模型分为两个部分，并分别在用户设备（UE）和Edge Server上执行它们，从而变得有吸引力。但是，DNN的大型中间特征会阻碍灵活的脱钩，现有方法要么集中在单个UE方案上，要么只是在考虑所需的CPU周期的情况下定义任务，但忽略了单个DNN层的不可分割性。在本文中，我们研究了多代理协作推理方案，其中单个边缘服务器协调了多个UES的推理。我们的目标是为所有UES实现快速和节能的推断。为了实现这一目标，我们首先设计了一种基于自动编码器的轻型方法，以压缩大型中间功能。然后，我们根据DNN的推理开销定义任务，并将问题作为马尔可夫决策过程（MDP）。最后，我们提出了一种多代理混合近端策略优化（MAHPPO）算法，以解决混合动作空间的优化问题。我们对不同类型的网络进行了广泛的实验，结果表明，我们的方法可以降低56％的推理潜伏期，并节省多达72 \％的能源消耗。

In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience. In this article, we propose two frameworks to support automatic medical consultation, namely doctor-patient dialogue understanding and task-oriented interaction. We create a new large medical dialogue dataset with multi-level finegrained annotations and establish five independent tasks, including named entity recognition, dialogue act classification, symptom label inference, medical report generation and diagnosis-oriented dialogue policy. We report a set of benchmark results for each task, which shows the usability of the dataset and sets a baseline for future studies. Both code and data is available from https://github.com/lemuria-wchen/imcs21.

现在，推荐系统已经变得繁荣，旨在通过学习嵌入来预测用户对项目的潜在兴趣。图形神经网络的最新进展〜（GNNS）还提供带有强大备份的推荐系统，从用户项图中学习嵌入。但是，由于数据收集困难，仅利用用户项交互遭受冷启动问题。因此，目前的努力建议将社交信息与用户项目相互作用融合以缓解它，这是社会推荐问题。现有工作使用GNNS同时聚合两个社交链接和用户项交互。但是，它们都需要集中存储的社交链接和用户的互动，从而导致隐私问题。此外，根据严格的隐私保护，在一般数据保护规则下，将来可能不可行的数据存储可能是不可行的，敦促分散的社会建议框架。为此，我们设计了一个小说框架\ textbf {fe} delated \ textbf {so} cial推荐与\ textbf {g} raph神经网络（fesog）。首先，FeSog采用关系的关注和聚集来处理异质性。其次，Fesog Infers使用本地数据来保留个性化的用户嵌入。最后但并非最不重要的是，所提出的模型采用伪标签技术，其中包含项目采样，以保护隐私和增强培训。三个现实世界数据集的广泛实验可以证明FeSog在完成社会建议和隐私保护方面的有效性。我们是为我们所知，为社会建议提供联邦学习框架的第一项工作。

3D视觉输入的对象操纵对构建可宽大的感知和政策模型构成了许多挑战。然而，现有基准中的3D资产主要缺乏与拓扑和几何中的现实世界内复杂的3D形状的多样性。在这里，我们提出了Sapien操纵技能基准（Manishill）以在全物理模拟器中的各种物体上基准操纵技巧。 Manishill中的3D资产包括大型课堂内拓扑和几何变化。仔细选择任务以涵盖不同类型的操纵挑战。 3D Vision的最新进展也使我们认为我们应该定制基准，以便挑战旨在邀请研究3D深入学习的研究人员。为此，我们模拟了一个移动的全景摄像头，返回以自我为中心的点云或RGB-D图像。此外，我们希望Manishill是为一个对操纵研究感兴趣的广泛研究人员提供服务。除了支持从互动的政策学习，我们还支持学习 - 从演示（LFD）方法，通过提供大量的高质量演示（〜36,000个成功的轨迹，总共〜1.5米点云/ RGB-D帧）。我们提供使用3D深度学习和LFD算法的基线。我们的基准（模拟器，环境，SDK和基线）的所有代码都是开放的，并且将基于基准举办跨学科研究人员面临的挑战。

As one of the most important psychic stress reactions, micro-expressions (MEs), are spontaneous and transient facial expressions that can reveal the genuine emotions of human beings. Thus, recognizing MEs (MER) automatically is becoming increasingly crucial in the field of affective computing, and provides essential technical support in lie detection, psychological analysis and other areas. However, the lack of abundant ME data seriously restricts the development of cutting-edge data-driven MER models. Despite the recent efforts of several spontaneous ME datasets to alleviate this problem, it is still a tiny amount of work. To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7,526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years. Afterwards, we adopt four classical spatiotemporal feature learning models on DFME to perform MER experiments to objectively verify the validity of DFME dataset. In addition, we explore different solutions to the class imbalance and key-frame sequence sampling problems in dynamic MER respectively on DFME, so as to provide a valuable reference for future research. The comprehensive experimental results show that our DFME dataset can facilitate the research of automatic MER, and provide a new benchmark for MER. DFME will be published via https://mea-lab-421.github.io.

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.

Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes), which has attracted increasing attention from the multimedia and computer vision communities. Prior methods successfully preserve the character of clothing images, however, occlusion remains a pernicious effect for realistic virtual try-on. In this work, we first present a comprehensive analysis of the occlusions and categorize them into two aspects: i) Inherent-Occlusion: the ghost of the former cloth still exists in the try-on image; ii) Acquired-Occlusion: the target cloth warps to the unreasonable body part. Based on the in-depth analysis, we find that the occlusions can be simulated by a novel semantically-guided mixup module, which can generate semantic-specific occluded images that work together with the try-on images to facilitate training a de-occlusion try-on (DOC-VTON) framework. Specifically, DOC-VTON first conducts a sharpened semantic parsing on the try-on person. Aided by semantics guidance and pose prior, various complexities of texture are selectively blending with human parts in a copy-and-paste manner. Then, the Generative Module (GM) is utilized to take charge of synthesizing the final try-on image and learning to de-occlusion jointly. In comparison to the state-of-the-art methods, DOC-VTON achieves better perceptual quality by reducing occlusion effects.