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.

Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL. This work focuses on the former. Previous methods build the network with several modules like CNN, LSTM and Attention. Recent methods combine the Transformer with these modules for better performance. However, it requires tedious optimization skills to train a network composed of mixed modules, making these methods inconvenient to be used in practice. In this paper, we propose to design \emph{pure Transformer-based networks} for deep RL, aiming at providing off-the-shelf backbones for both the online and offline settings. Specifically, the Transformer in Transformer (TIT) backbone is proposed, which cascades two Transformers in a very natural way: the inner one is used to process a single observation, while the outer one is responsible for processing the observation history; combining both is expected to extract spatial-temporal representations for good decision-making. Experiments show that TIT can achieve satisfactory performance in different settings, consistently.

Classification using supervised learning requires annotating a large amount of classes-balanced data for model training and testing. This has practically limited the scope of applications with supervised learning, in particular deep learning. To address the issues associated with limited and imbalanced data, this paper introduces a sample-efficient co-supervised learning paradigm (SEC-CGAN), in which a conditional generative adversarial network (CGAN) is trained alongside the classifier and supplements semantics-conditioned, confidence-aware synthesized examples to the annotated data during the training process. In this setting, the CGAN not only serves as a co-supervisor but also provides complementary quality examples to aid the classifier training in an end-to-end fashion. Experiments demonstrate that the proposed SEC-CGAN outperforms the external classifier GAN (EC-GAN) and a baseline ResNet-18 classifier. For the comparison, all classifiers in above methods adopt the ResNet-18 architecture as the backbone. Particularly, for the Street View House Numbers dataset, using the 5% of training data, a test accuracy of 90.26% is achieved by SEC-CGAN as opposed to 88.59% by EC-GAN and 87.17% by the baseline classifier; for the highway image dataset, using the 10% of training data, a test accuracy of 98.27% is achieved by SEC-CGAN, compared to 97.84% by EC-GAN and 95.52% by the baseline classifier.

基于自动机的方法使机器人能够执行各种复杂的任务。但是，大多数现有的基于自动机的算法都高度依赖于已考虑任务的状态的手动定制表示，从而限制了其在深度强化学习算法中的适用性。为了解决这个问题，通过将变压器纳入强化学习中，我们开发了一个双转化器引导的时间逻辑框架（T2TL），该逻辑框架（T2TL）两次利用变压器的结构特征，即首先通过变压器模块编码LTL指令，以有效地理解对有效的理解培训期间的任务说明，然后再次通过变压器编码上下文变量，以改善任务性能。特别是，LTL指令由Co-Safe LTL指定。作为具有语义的改写操作，LTL的进展被利用以将复杂的任务分解为可学习的子目标，这不仅将非马克维亚奖励决策转换为马尔可夫的奖励决策过程，而且通过同时学习多个子 - 学习效率，提高了采样效率。任务。进一步纳入了环境不足的LTL预训练方案，以促进变压器模块的学习，从而改善LTL的表示。模拟和实验结果证明了T2TL框架的有效性。

随着自动假新闻检测技术的快速发展，事实提取和验证（发烧）吸引了更多的关注。该任务旨在从数百万个开放域Wikipedia文件中提取最相关的事实证据，然后验证相应索赔的可信度。尽管已经为该任务提出了几种强大的模型，但他们取得了长足的进步，但我们认为他们无法利用多视图上下文信息，因此无法获得更好的性能。在本文中，我们建议整合多视图上下文信息（IMCI）进行事实提取和验证。对于每个证据句子，我们定义两种上下文，即文档内部上下文和文档间的上下文}。文档内上下文由文档标题和同一文档中的所有其他句子组成。文档间的上下文包括所有其他证据，这些证据可能来自不同的文档。然后，我们集成了多视图上下文信息，以编码证据句子以处理任务。我们对发烧1.0共享任务的实验结果表明，我们的IMCI框架在事实提取和验证方面取得了长足的进步，并以72.97％的胜利发烧得分达到了最先进的表现，在线上获得了75.84％的标签准确度盲测。我们还进行消融研究以检测多视图上下文信息的影响。我们的代码将在https://github.com/phoenixsecularbird/imci上发布。

面向目标的意见单词提取（TOWE）是一项精细的情感分析任务，旨在从句子中提取给定意见目标的相应意见单词。最近，深度学习方法在这项任务上取得了显着进步。然而，由于昂贵的数据注释过程，TOWE任务仍然遭受培训数据的稀缺性。有限的标记数据增加了测试数据和培训数据之间分配变化的风险。在本文中，我们建议利用大量未标记的数据来通过增加模型对变化分布变化的暴露来降低风险。具体而言，我们提出了一种新型的多透明一致性正则化（MGCR）方法，以利用未标记的数据并设计两个专门用于TOWE的过滤器，以在不同的粒度上过滤嘈杂的数据。四个TOWE基准数据集的广泛实验结果表明，与当前的最新方法相比，MGCR的优越性。深入分析还证明了不同粒度过滤器的有效性。我们的代码可在https://github.com/towessl/towessl上找到。

半监督学习（SSL）通过利用大量未标记数据来增强有限标记的样品来改善模型的概括。但是，目前，流行的SSL评估协议通常受到计算机视觉（CV）任务的约束。此外，以前的工作通常从头开始训练深层神经网络，这是耗时且环境不友好的。为了解决上述问题，我们通过从简历，自然语言处理（NLP）和音频处理（AUDIO）中选择15种不同，具有挑战性和全面的任务来构建统一的SSL基准（USB），我们会系统地评估主导的SSL方法，以及开源的一个模块化和可扩展的代码库，以对这些SSL方法进行公平评估。我们进一步为简历任务提供了最新的神经模型的预训练版本，以使成本负担得起，以进行进一步调整。 USB启用对来自多个域的更多任务的单个SSL算法的评估，但成本较低。具体而言，在单个NVIDIA V100上，仅需要37个GPU天才能在USB中评估15个任务的FIXMATCH，而335 GPU天（除ImageNet以外的4个CV数据集中的279 GPU天）在使用典型协议的5个CV任务上需要进行5个CV任务。

在分支机构和结合中得出良好的可变选择策略对于现代混合编程（MIP）求解器的效率至关重要。通过在先前的解决方案过程中收集的MIP分支数据，学习分支方法最近变得比启发式方法更好。由于分支机构自然是一项顺序决策任务，因此应该学会优化整个MIP求解过程的实用性，而不是在每个步骤上都是近视。在这项工作中，我们将学习作为离线增强学习（RL）问题进行分支，并提出了一种长期视线的混合搜索方案来构建离线MIP数据集，该数据集对分支决策的长期实用程序。在政策培训阶段，我们部署了基于排名的奖励分配计划，以将有希望的样本与长期或短期视图区分开，并通过离线政策学习训练名为分支排名的分支模型。合成MIP基准和现实世界任务的实验表明，与广泛使用的启发式方法和基于先进的学习分支模型相比，分支rankink更有效，更健壮，并且可以更好地概括为MIP实例的大型MIP实例。

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.

学习协作对于多机构增强学习（MARL）至关重要。以前的作品通过最大化代理行为的相关性来促进协作，该行为的相关性通常以不同形式的相互信息（MI）为特征。但是，我们揭示了次最佳的协作行为，也出现了强烈的相关性，并且简单地最大化MI可以阻碍学习的学习能力。为了解决这个问题，我们提出了一个新颖的MARL框架，称为“渐进式信息协作（PMIC）”，以进行更有效的MI驱动协作。 PMIC使用全球国家和联合行动之间MI测量的新协作标准。基于此标准，PMIC的关键思想是最大程度地提高与优越的协作行为相关的MI，并最大程度地减少与下等方面相关的MI。这两个MI目标通过促进更好的合作，同时避免陷入次级优势，从而扮演互补的角色。与其他算法相比，在各种MARL基准测试的实验表明，PMIC的表现出色。