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 super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt / 30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper.

不兼容的可观察物的存在是量子力学和量子技术中宝贵资源的基石。在这里，我们介绍了一种不兼容的度量，称为相互特征空间扰动（MED），该措施量化了通过在另一个人的特征范围内观察到的尖锐观察到的敏锐的干扰量。 MED是对尖锐可观察物的忠实衡量标准，并在von Neumann测量空间上提供了度量。可以通过使用称为量子开关的设置以无限期的顺序使测量作用来有效地估计。由于这些功能，MED可以用于量子机学习任务中，例如基于它们相互兼容性的量子测量设备。我们通过提供无监督的算法来证明这种应用，该算法将未知的von Neumann测量结果簇。我们的算法对噪声非常强大，可用于识别具有大致相同测量环境的观察者组。

任务概括是自然语言处理（NLP）的漫长挑战。最近的研究试图通过将NLP任务映射到人类可读的提示形式中来提高预训练语言模型的任务概括能力。但是，这些方法需要费力且不灵活的提示，并且在同一下游任务上的不同提示可能会获得不稳定的性能。我们提出了统一的架构提示，这是一种灵活且可扩展的提示方法，该方法会根据任务输入架构自动自动自定义每个任务的可学习提示。它在任务之间建模共享知识，同时保持不同任务架构的特征，从而增强任务概括能力。架构提示采用每个任务的明确数据结构，以制定提示，因此涉及几乎没有人类的努力。为了测试模式提示的任务概括能力，我们对各种一般NLP任务进行基于模式提示的多任务预训练。该框架在从8种任务类型（例如QA，NLI等）的16个看不见的下游任务上实现了强劲的零射击和很少的概括性能。此外，全面的分析证明了每个组件在架构提示中的有效性，其在任务组成性方面的灵活性以及在全DATA微调设置下提高性能的能力。

我们提出了一种新型的元学习方法，用于对未知物体的6D姿势估计。与“实例级”构成估计方法相反，我们的算法以类别 - 不合命相的方式学习对象表示，从而在对象类别中赋予其具有强大的概括能力。具体而言，我们采用条件神经过程的元学习方法来训练编码器，以基于很少的RGB-D图像和地面真实关键点，以潜在表示中捕获对象的纹理和几何形状。然后，同时进行元训练的解码器使用潜在表示，以预测新图像中对象的6D姿势。为了评估我们的算法，在多个场景（MCMS）中从多个类别生成的新的全通道合成数据集进行了实验。实验结果表明，我们的模型在具有各种形状和外观的看不见的物体上表现良好。

Question Answering (QA) is a longstanding challenge in natural language processing. Existing QA works mostly focus on specific question types, knowledge domains, or reasoning skills. The specialty in QA research hinders systems from modeling commonalities between tasks and generalization for wider applications. To address this issue, we present ProQA, a unified QA paradigm that solves various tasks through a single model. ProQA takes a unified structural prompt as the bridge and improves the QA-centric ability by structural prompt-based pre-training. Through a structurally designed prompt-based input schema, ProQA concurrently models the knowledge generalization for all QA tasks while keeping the knowledge customization for every specific QA task. Furthermore, ProQA is pre-trained with structural prompt-formatted large-scale synthesized corpus, which empowers the model with the commonly-required QA ability. Experimental results on 11 QA benchmarks demonstrate that ProQA consistently boosts performance on both full data fine-tuning, few-shot learning, and zero-shot testing scenarios. Furthermore, ProQA exhibits strong ability in both continual learning and transfer learning by taking the advantages of the structural prompt.

在预先建立的3D环境图中，高精度摄像头重新定位技术是许多任务的基础，例如增强现实，机器人技术和自动驾驶。近几十年来，基于点的视觉重新定位方法已经发达了，但在某些不足的情况下不足。在本文中，我们设计了一条完整的管道，用于使用点和线的相机姿势完善，其中包含创新设计的生产线提取CNN，名为VLSE，线匹配和姿势优化方法。我们采用新颖的线表示，并根据堆叠的沙漏网络自定义混合卷积块，以检测图像上的准确稳定的线路功能。然后，我们采用基于几何的策略，使用表极约束和再投影过滤获得精确的2D-3D线对应关系。构建了以下点线关节成本函数，以通过基于纯点的本地化的初始粗姿势优化相机姿势。在开放数据集（即线框上的线提取器）上进行了足够的实验，在INLOC DUC1和DUC2上的定位性能，以确认我们的点线关节姿势优化方法的有效性。

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.

In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.