随着向设备深度学习的转变，确保在各种计算平台上的AI服务的一致行为变得非常重要。我们的工作解决了降低视力倒数的预测不一致的新兴问题：由较不准确的模型正确预测但错误地预测的测试样品。我们介绍了回归约束的神经体系结构搜索（Reg-NAS），以设计一个高度准确的模型家庭，这些模型会导致更少的负面流动。 Reg-NAS由两个组成部分组成：（1）一种新型的体系结构约束，使较大的模型能够包含较小的权重，从而最大化权重共享。这一想法源于我们的观察结果，即网络之间的重量较大会导致相似的样本预测，并导致负面量较少。 （2）一种新颖的搜索奖励，在体系结构搜索指标中同时结合了TOP-1的准确性和负面翻转。我们证明，\ regnas可以在三个流行的架构搜索空间中成功找到具有很少负面额的理想体系结构。与现有的最新方法相比，Reg-NAS可实现33-48％的负面流量相对减少。

深度学习模型已在大规模视频基准测试上取得了出色的识别结果。但是，当应用于稀有场景或物体的视频时，它们的性能很差，这主要是由于现有视频数据集的偏见。我们从两个不同的角度解决了这个问题：算法和数据集。从算法的角度来看，我们提出了空间感知的多种偏见（SMAD），它既将明确的偏见都与多种相对的对抗性训练和隐含的偏见以及与空间行动重新重量的模块相结合，从行动方面。为了消除内在的数据集偏差，我们建议OmnideBias有选择地利用Web数据进行联合培训，这可以通过更少的Web数据实现更高的性能。为了验证有效性，我们建立评估协议并对现有数据集的重新分配分配和新的评估数据集进行广泛的实验，该数据集的重点是稀有场景。我们还表明，当转移到其他数据集和任务时，辩护形式可以更好地概括。

我们呈现了对比邻域对准（CNA），一种歧管学习方法来维持学习特征的拓扑，由此映射到源（教师）模型的附近表示的数据点也被目标（学生）模型映射到邻居。目标模型旨在模拟使用对比损耗来模拟源代表空间的局部结构。CNA是一种无人监督的学习算法，不需要对各个样本的地面真理标签。CNA在三种情况下示出：歧管学习，其中模型在尺寸减小空间中保持原始数据的本地拓扑;模型蒸馏，其中小学生模型培训以模仿更大的老师;和遗留模型更新，其中旧模型被更强大的更强大的型号。实验表明，CNA能够在高维空间中捕获歧管，并与其域中的竞争方法相比提高性能。

我们提出了块茎：一种简单的时空视频动作检测解决方案。与依赖于离线演员检测器或手工设计的演员位置假设的现有方法不同，我们建议通过同时执行动作定位和识别从单个表示来直接检测视频中的动作微管。块茎学习一组管芯查询，并利用微调模块来模拟视频剪辑的动态时空性质，其有效地加强了与在时空空间中的演员位置假设相比的模型容量。对于包含过渡状态或场景变更的视频，我们提出了一种上下文意识的分类头来利用短期和长期上下文来加强行动分类，以及用于检测精确的时间动作程度的动作开关回归头。块茎直接产生具有可变长度的动作管，甚至对长视频剪辑保持良好的结果。块茎在常用的动作检测数据集AVA，UCF101-24和JHMDB51-21上优于先前的最先进。

Dynamics of human body skeletons convey significant information for human action recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power and difficulties of generalization. In this work, we propose a novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data. This formulation not only leads to greater expressive power but also stronger generalization capability. On two large datasets, Kinetics and NTU-RGBD, it achieves substantial improvements over mainstream methods.

Detecting actions in untrimmed videos is an important yet challenging task. In this paper, we present the structured segment network (SSN), a novel framework which models the temporal structure of each action instance via a structured temporal pyramid. On top of the pyramid, we further introduce a decomposed discriminative model comprising two classifiers, respectively for classifying actions and determining completeness. This allows the framework to effectively distinguish positive proposals from background or incomplete ones, thus leading to both accurate recognition and localization. These components are integrated into a unified network that can be efficiently trained in an end-to-end fashion. Additionally, a simple yet effective temporal action proposal scheme, dubbed temporal actionness grouping (TAG) is devised to generate high quality action proposals. On two challenging benchmarks, THUMOS14 and ActivityNet, our method remarkably outperforms previous state-of-the-art methods, demonstrating superior accuracy and strong adaptivity in handling actions with various temporal structures. 1

Deep convolutional networks have achieved great success for visual recognition in still images. However, for action recognition in videos, the advantage over traditional methods is not so evident. This paper aims to discover the principles to design effective ConvNet architectures for action recognition in videos and learn these models given limited training samples. Our first contribution is temporal segment network (TSN), a novel framework for video-based action recognition. which is based on the idea of long-range temporal structure modeling. It combines a sparse temporal sampling strategy and video-level supervision to enable efficient and effective learning using the whole action video. The other contribution is our study on a series of good practices in learning ConvNets on video data with the help of temporal segment network. Our approach obtains the state-the-of-art performance on the datasets of HMDB51 (69.4%) and UCF101 (94.2%). We also visualize the learned ConvNet models, which qualitatively demonstrates the effectiveness of temporal segment network and the proposed good practices. 1

Given the increasingly intricate forms of partial differential equations (PDEs) in physics and related fields, computationally solving PDEs without analytic solutions inevitably suffers from the trade-off between accuracy and efficiency. Recent advances in neural operators, a kind of mesh-independent neural-network-based PDE solvers, have suggested the dawn of overcoming this challenge. In this emerging direction, Koopman neural operator (KNO) is a representative demonstration and outperforms other state-of-the-art alternatives in terms of accuracy and efficiency. Here we present KoopmanLab, a self-contained and user-friendly PyTorch module of the Koopman neural operator family for solving partial differential equations. Beyond the original version of KNO, we develop multiple new variants of KNO based on different neural network architectures to improve the general applicability of our module. These variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier-Stokes equation and the Bateman-Burgers equation) and ERA5 (i.e., one of the largest high-resolution data sets of global-scale climate fields). These demonstrations suggest the potential of KoopmanLab to be considered in diverse applications of partial differential equations.

Temporal sentence grounding (TSG) aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning. However, we argue that these methods have overlooked two indispensable issues: 1) Boundary-bias: The annotated target segment generally refers to two specific frames as corresponding start and end timestamps. The video downsampling process may lose these two frames and take the adjacent irrelevant frames as new boundaries. 2) Reasoning-bias: Such incorrect new boundary frames also lead to the reasoning bias during frame-query interaction, reducing the generalization ability of model. To alleviate above limitations, in this paper, we propose a novel Siamese Sampling and Reasoning Network (SSRN) for TSG, which introduces a siamese sampling mechanism to generate additional contextual frames to enrich and refine the new boundaries. Specifically, a reasoning strategy is developed to learn the inter-relationship among these frames and generate soft labels on boundaries for more accurate frame-query reasoning. Such mechanism is also able to supplement the absent consecutive visual semantics to the sampled sparse frames for fine-grained activity understanding. Extensive experiments demonstrate the effectiveness of SSRN on three challenging datasets.

Time-series anomaly detection is an important task and has been widely applied in the industry. Since manual data annotation is expensive and inefficient, most applications adopt unsupervised anomaly detection methods, but the results are usually sub-optimal and unsatisfactory to end customers. Weak supervision is a promising paradigm for obtaining considerable labels in a low-cost way, which enables the customers to label data by writing heuristic rules rather than annotating each instance individually. However, in the time-series domain, it is hard for people to write reasonable labeling functions as the time-series data is numerically continuous and difficult to be understood. In this paper, we propose a Label-Efficient Interactive Time-Series Anomaly Detection (LEIAD) system, which enables a user to improve the results of unsupervised anomaly detection by performing only a small amount of interactions with the system. To achieve this goal, the system integrates weak supervision and active learning collaboratively while generating labeling functions automatically using only a few labeled data. All of these techniques are complementary and can promote each other in a reinforced manner. We conduct experiments on three time-series anomaly detection datasets, demonstrating that the proposed system is superior to existing solutions in both weak supervision and active learning areas. Also, the system has been tested in a real scenario in industry to show its practicality.