开放信息提取（OpenIE）的最先进的神经方法通常以自回旋或基于谓词的方式迭代地提取三重态（或元组），以免产生重复。在这项工作中，我们提出了一种可以平等或更成功的问题的不同方法。也就是说，我们提出了一种新型的单通道方法，用于开放式启发，该方法受到计算机视觉的对象检测算法的启发。我们使用基于双方匹配的订单不足损失，迫使独特的预测和用于序列标签的仅基于变压器的纯编码体系结构。与质量指标和推理时间相比，与标准基准的最新模型相比，提出的方法更快，并且表现出卓越或类似的性能。我们的模型在CARB上的新最新性能为OIE2016评估，而推断的速度比以前的最新状态更快。我们还在两种语言的零弹奏设置中评估了模型的多语言版本，并引入了一种生成合成多语言数据的策略，以微调每个特定语言的模型。在这种情况下，我们在多语言Re-OIE2016上显示了15％的性能提高，葡萄牙语和西班牙语的F1达到75％。代码和型号可在https://github.com/sberbank-ai/detie上找到。

在这项工作中，我们探讨了在线评论的建设性方面：用户提供有关商品，场所，服务和其他感兴趣物品的咨询，提示，请求和建议。为降低构建特定标签集的分类器所需的培训成本和注释工作，我们展示并评估了以标签完全看不见的方式提出了几种基于意外的零点零点方法来提出了建议分类。特别是，我们介绍了将目标类标签分配给英语语言的句子的策略，用户意图显着提高了预测质量。拟议的策略是通过全面的实验研究评估，验证了我们的定量和定性的结果。

自动监测不良药物事件（ADES）或反应（ADRS）目前正在从生物医学界获得重大关注。近年来，用户生成的社交媒体数据已成为这项任务的宝贵资源。神经模型对ADR检测的自动文本分类取得了令人印象深刻的性能。然而，对这些方法的培训和评估是对关于目标药物的用户生成的文本进行。在本文中，我们评估了不同药物组的最先进神经结构的鲁棒性。除了手动注释的列车集外，我们还调查了几种使用伪标记的数据的策略。在数据集外部实验中诊断了监督模型的瓶颈在故障性能方面，而额外的伪标记数据无论文本选择策略如何，都会提高整体结果。

支持II社区的当前趋势，我们提出了一个称为融合大脑的AI Journey 2021挑战，这些挑战是融合大脑，该挑战是使普通架构处理不同的方式（即图像，文本和代码），并解决视觉和语言的多个任务。融合脑挑战https://github.com/sberbank- ai/fusion_brain_aij2021结合了以下特定任务：code2code翻译，手写文本识别，零拍摄对象检测和视觉问题应答。我们为每个任务创建了数据集以测试参与者的提交。此外，我们在俄语和英语中开设了一个新的手写数据集，其中包含94,130对图像和文本。DataSet的俄罗斯部分是世界上最大的俄罗斯手写数据集。我们还提出了基线解决方案和相应的特定于任务特定解决方案以及整体指标。

Objective: Accurate visual classification of bladder tissue during Trans-Urethral Resection of Bladder Tumor (TURBT) procedures is essential to improve early cancer diagnosis and treatment. During TURBT interventions, White Light Imaging (WLI) and Narrow Band Imaging (NBI) techniques are used for lesion detection. Each imaging technique provides diverse visual information that allows clinicians to identify and classify cancerous lesions. Computer vision methods that use both imaging techniques could improve endoscopic diagnosis. We address the challenge of tissue classification when annotations are available only in one domain, in our case WLI, and the endoscopic images correspond to an unpaired dataset, i.e. there is no exact equivalent for every image in both NBI and WLI domains. Method: We propose a semi-surprised Generative Adversarial Network (GAN)-based method composed of three main components: a teacher network trained on the labeled WLI data; a cycle-consistency GAN to perform unpaired image-to-image translation, and a multi-input student network. To ensure the quality of the synthetic images generated by the proposed GAN we perform a detailed quantitative, and qualitative analysis with the help of specialists. Conclusion: The overall average classification accuracy, precision, and recall obtained with the proposed method for tissue classification are 0.90, 0.88, and 0.89 respectively, while the same metrics obtained in the unlabeled domain (NBI) are 0.92, 0.64, and 0.94 respectively. The quality of the generated images is reliable enough to deceive specialists. Significance: This study shows the potential of using semi-supervised GAN-based classification to improve bladder tissue classification when annotations are limited in multi-domain data.

The receptive field (RF), which determines the region of time series to be ``seen'' and used, is critical to improve the performance for time series classification (TSC). However, the variation of signal scales across and within time series data, makes it challenging to decide on proper RF sizes for TSC. In this paper, we propose a dynamic sparse network (DSN) with sparse connections for TSC, which can learn to cover various RF without cumbersome hyper-parameters tuning. The kernels in each sparse layer are sparse and can be explored under the constraint regions by dynamic sparse training, which makes it possible to reduce the resource cost. The experimental results show that the proposed DSN model can achieve state-of-art performance on both univariate and multivariate TSC datasets with less than 50\% computational cost compared with recent baseline methods, opening the path towards more accurate resource-aware methods for time series analyses. Our code is publicly available at: https://github.com/QiaoXiao7282/DSN.

While the problem of hallucinations in neural machine translation has long been recognized, so far the progress on its alleviation is very little. Indeed, recently it turned out that without artificially encouraging models to hallucinate, previously existing methods fall short and even the standard sequence log-probability is more informative. It means that characteristics internal to the model can give much more information than we expect, and before using external models and measures, we first need to ask: how far can we go if we use nothing but the translation model itself ? We propose to use a method that evaluates the percentage of the source contribution to a generated translation. Intuitively, hallucinations are translations "detached" from the source, hence they can be identified by low source contribution. This method improves detection accuracy for the most severe hallucinations by a factor of 2 and is able to alleviate hallucinations at test time on par with the previous best approach that relies on external models. Next, if we move away from internal model characteristics and allow external tools, we show that using sentence similarity from cross-lingual embeddings further improves these results.

We pose video object segmentation as spectral graph clustering in space and time, with one graph node for each pixel and edges forming local space-time neighborhoods. We claim that the strongest cluster in this video graph represents the salient object. We start by introducing a novel and efficient method based on 3D filtering for approximating the spectral solution, as the principal eigenvector of the graph's adjacency matrix, without explicitly building the matrix. This key property allows us to have a fast parallel implementation on GPU, orders of magnitude faster than classical approaches for computing the eigenvector. Our motivation for a spectral space-time clustering approach, unique in video semantic segmentation literature, is that such clustering is dedicated to preserving object consistency over time, which we evaluate using our novel segmentation consistency measure. Further on, we show how to efficiently learn the solution over multiple input feature channels. Finally, we extend the formulation of our approach beyond the segmentation task, into the realm of object tracking. In extensive experiments we show significant improvements over top methods, as well as over powerful ensembles that combine them, achieving state-of-the-art on multiple benchmarks, both for tracking and segmentation.

Metric Elicitation (ME) is a framework for eliciting classification metrics that better align with implicit user preferences based on the task and context. The existing ME strategy so far is based on the assumption that users can most easily provide preference feedback over classifier statistics such as confusion matrices. This work examines ME, by providing a first ever implementation of the ME strategy. Specifically, we create a web-based ME interface and conduct a user study that elicits users' preferred metrics in a binary classification setting. We discuss the study findings and present guidelines for future research in this direction.

Learning-based image compression has improved to a level where it can outperform traditional image codecs such as HEVC and VVC in terms of coding performance. In addition to good compression performance, device interoperability is essential for a compression codec to be deployed, i.e., encoding and decoding on different CPUs or GPUs should be error-free and with negligible performance reduction. In this paper, we present a method to solve the device interoperability problem of a state-of-the-art image compression network. We implement quantization to entropy networks which output entropy parameters. We suggest a simple method which can ensure cross-platform encoding and decoding, and can be implemented quickly with minor performance deviation, of 0.3% BD-rate, from floating point model results.