Learning effective motion features is an essential pursuit of video representation learning. This paper presents a simple yet effective sample construction strategy to boost the learning of motion features in video contrastive learning. The proposed method, dubbed Motion-focused Quadruple Construction (MoQuad), augments the instance discrimination by meticulously disturbing the appearance and motion of both the positive and negative samples to create a quadruple for each video instance, such that the model is encouraged to exploit motion information. Unlike recent approaches that create extra auxiliary tasks for learning motion features or apply explicit temporal modelling, our method keeps the simple and clean contrastive learning paradigm (i.e.,SimCLR) without multi-task learning or extra modelling. In addition, we design two extra training strategies by analyzing initial MoQuad experiments. By simply applying MoQuad to SimCLR, extensive experiments show that we achieve superior performance on downstream tasks compared to the state of the arts. Notably, on the UCF-101 action recognition task, we achieve 93.7% accuracy after pre-training the model on Kinetics-400 for only 200 epochs, surpassing various previous methods

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.

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of <instrument, verb, target> combination delivers comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms by competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery.

在本文中，我们开发了一种新的更改检测算法，用于检测马尔可夫内核在变化后内核未知的度量空间上的变化。假设Markov前和后马尔可夫内核是均匀的，我们在误报之间的平均延迟和下限上得出了上限。提供了数值模拟以证明我们方法的有效性。

基于梯度的残差训练的方法（RESNET）通常需要输入数据的前进通过，然后将误差梯度反向传播到更新模型参数，这变得耗费网络变得更深。为了破坏前向和向后模式的算法锁定和利用同步模块并行性，辅助变量方法最近吸引了很多兴趣，但遭受了重大的沟通开销和缺乏数据增强。在这项工作中，通过交易外部辅助变量的存储和重新计算，建立了一种用于跨多个计算设备训练现实Resnet的新颖联合学习框架。更具体地，每个独立处理器的输入数据是从其低容量辅助网络（AUXNET）生成的，这允许使用数据增强并实现前向解锁。然后并行地执行后向通过，每个丢失函数源自惩罚或增强拉格朗日（AL）方法。最后，采用所提出的AUXNET通过端到端培训过程重现更新的辅助变量。我们展示了我们在CIFAR-10，CIFAR-100和Imagenet数据集中展示了我们对RESNET和WIMERESNET的效果，实现了传统的层串行训练方法的加速，同时保持了可比的测试精度。

来自结构数据的自然语言生成主要侧重于表面级描述，患有无法控制的内容选择和低保真度。以前的作品利用逻辑表格来促进逻辑知识条件文本生成。虽然取得了显着的进步，但它们是数据饥饿的，这使得通过有限的数据充分利用现实应用程序。为此，本文提出了几次拍摄设置中的逻辑知识条件文本生成的统一框架。只有少量种子逻辑形式（例如，20/100拍摄），我们的方法利用自我训练和样本伪逻辑形式，基于内容和结构一致性。实验结果表明，我们的方法可以比基线获得更好的少量表现。

本文介绍了一种用于结构重建的新型关注的神经网络，其将2D光栅图像作为输入，并重建描绘底层几何结构的平面图。该方法检测角落，并以端到端的方式对角之间进行分类边缘候选。我们的贡献是整体边缘分类架构，其中1）通过其端点的三角位置编码初始化边缘候选的特征; 2）通过可变形的关注将图像特征融合到每个边缘候选; 3）采用两个重量分配变压器解码器，用于在图形边缘候选方面学习整体结构模式; 4）通过掩盖的学习策略培训。拐角探测器是边缘分类架构的变体，适用于作为转角候选的像素上操作。我们对两种结构化重建任务进行实验：户外建筑架构和室内平面平面图形重建。广泛的定性和量化评估表明了我们对现有技术的方法的优势。我们将分享代码和模型。

几次拍摄的语义分割旨在将新颖的类对象分段为仅具有少数标记的支持图像。大多数高级解决方案利用度量学习框架，通过将每个查询功能与学习的类特定的原型匹配来执行分段。然而，由于特征比较不完整，该框架遭受了偏见的分类。为了解决这个问题，我们通过引入类别特定的和类别不可知的原型来提出自适应原型表示，从而构建与查询功能学习语义对齐的完整样本对。互补特征学习方式有效地丰富了特征比较，并有助于在几次拍摄设置中产生一个非偏见的分段模型。它用双分支端到端网络（\即，特定于类分支和类别不可知分支）实现，它生成原型，然后组合查询特征以执行比较。此外，所提出的类别无神不可话的分支简单而且有效。在实践中，它可以自适应地为查询图像生成多种类别 - 不可知的原型，并以自我对比方式学习特征对齐。广泛的Pascal-5 $ ^ i $和Coco-20 $ ^ i $展示了我们方法的优越性。在不牺牲推理效率的费用中，我们的模型实现了最先进的，导致1-Shot和5-Shot Settings进行语义分割。

Despite the surprising few-shot performance of in-context learning (ICL), it is still a common practice to randomly sample examples to serve as context. This paper advocates a new principle for ICL: self-adaptive in-context learning. The self-adaption mechanism is introduced to help each sample find an in-context example permutation (i.e., selection and ordering) that can derive the correct prediction, thus maximizing performance. To validate the effectiveness of self-adaptive ICL, we propose a general select-then-rank framework and instantiate it with new selection and ranking algorithms. Upon extensive evaluation on eight different NLP datasets, our self-adaptive ICL method achieves a 40% relative improvement over the common practice setting. Further analysis reveals the enormous potential of self-adaptive ICL that it might be able to close the gap between ICL and finetuning given more advanced algorithms. Our code is released to facilitate future research in this area: https://github.com/Shark-NLP/self-adaptive-ICL