In this paper, we develop an efficient multi-scale network to predict action classes in partial videos in an end-to-end manner. Unlike most existing methods with offline feature generation, our method directly takes frames as input and further models motion evolution on two different temporal scales.Therefore, we solve the complexity problems of the two stages of modeling and the problem of insufficient temporal and spatial information of a single scale. Our proposed End-to-End MultiScale Network (E2EMSNet) is composed of two scales which are named segment scale and observed global scale. The segment scale leverages temporal difference over consecutive frames for finer motion patterns by supplying 2D convolutions. For observed global scale, a Long Short-Term Memory (LSTM) is incorporated to capture motion features of observed frames. Our model provides a simple and efficient modeling framework with a small computational cost. Our E2EMSNet is evaluated on three challenging datasets: BIT, HMDB51, and UCF101. The extensive experiments demonstrate the effectiveness of our method for action prediction in videos.

人类相互作用的分析是人类运动分析的一个重要研究主题。它已经使用第一人称视觉（FPV）或第三人称视觉（TPV）进行了研究。但是，到目前为止，两种视野的联合学习几乎没有引起关注。原因之一是缺乏涵盖FPV和TPV的合适数据集。此外，FPV或TPV的现有基准数据集具有多个限制，包括样本数量有限，参与者，交互类别和模态。在这项工作中，我们贡献了一个大规模的人类交互数据集，即FT-HID数据集。 FT-HID包含第一人称和第三人称愿景的成对对齐的样本。该数据集是从109个不同受试者中收集的，并具有三种模式的90K样品。该数据集已通过使用几种现有的动作识别方法验证。此外，我们还引入了一种新型的骨骼序列的多视图交互机制，以及针对第一人称和第三人称视野的联合学习多流框架。两种方法都在FT-HID数据集上产生有希望的结果。可以预期，这一视力一致的大规模数据集的引入将促进FPV和TPV的发展，以及他们用于人类行动分析的联合学习技术。该数据集和代码可在\ href {https://github.com/endlichere/ft-hid} {here} {herefichub.com/endlichere.com/endlichere}中获得。

Sign language recognition (SLR) aims to overcome the communication barrier for the people with deafness or the people with hard hearing. Most existing approaches can be typically divided into two lines, i.e., Skeleton-based and RGB-based methods, but both the two lines of methods have their limitations. RGB-based approaches usually overlook the fine-grained hand structure, while Skeleton-based methods do not take the facial expression into account. In attempts to address both limitations, we propose a new framework named Spatial-temporal Part-aware network (StepNet), based on RGB parts. As the name implies, StepNet consists of two modules: Part-level Spatial Modeling and Part-level Temporal Modeling. Particularly, without using any keypoint-level annotations, Part-level Spatial Modeling implicitly captures the appearance-based properties, such as hands and faces, in the feature space. On the other hand, Part-level Temporal Modeling captures the pertinent properties over time by implicitly mining the long-short term context. Extensive experiments show that our StepNet, thanks to Spatial-temporal modules, achieves competitive Top-1 Per-instance accuracy on three widely-used SLR benchmarks, i.e., 56.89% on WLASL, 77.2% on NMFs-CSL, and 77.1% on BOBSL. Moreover, the proposed method is compatible with the optical flow input, and can yield higher performance if fused. We hope that this work can serve as a preliminary step for the people with deafness.

有效地对视频中的空间信息进行建模对于动作识别至关重要。为了实现这一目标，最先进的方法通常采用卷积操作员和密集的相互作用模块，例如非本地块。但是，这些方法无法准确地符合视频中的各种事件。一方面，采用的卷积是有固定尺度的，因此在各种尺度的事件中挣扎。另一方面，密集的相互作用建模范式仅在动作 - 欧元零件时实现次优性能，给最终预测带来了其他噪音。在本文中，我们提出了一个统一的动作识别框架，以通过引入以下设计来研究视频内容的动态性质。首先，在提取本地提示时，我们会生成动态尺度的时空内核，以适应各种事件。其次，为了将这些线索准确地汇总为全局视频表示形式，我们建议仅通过变压器在一些选定的前景对象之间进行交互，从而产生稀疏的范式。我们将提出的框架称为事件自适应网络（EAN），因为这两个关键设计都适应输入视频内容。为了利用本地细分市场内的短期运动，我们提出了一种新颖有效的潜在运动代码（LMC）模块，进一步改善了框架的性能。在几个大规模视频数据集上进行了广泛的实验，例如，某种东西，动力学和潜水48，验证了我们的模型是否在低拖鞋上实现了最先进或竞争性的表演。代码可在：https：//github.com/tianyuan168326/ean-pytorch中找到。

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

Spatiotemporal and motion features are two complementary and crucial information for video action recognition. Recent state-of-the-art methods adopt a 3D CNN stream to learn spatiotemporal features and another flow stream to learn motion features. In this work, we aim to efficiently encode these two features in a unified 2D framework. To this end, we first propose an STM block, which contains a Channel-wise SpatioTemporal Module (CSTM) to present the spatiotemporal features and a Channel-wise Motion Module (CMM) to efficiently encode motion features. We then replace original residual blocks in the ResNet architecture with STM blcoks to form a simple yet effective STM network by introducing very limited extra computation cost. Extensive experiments demonstrate that the proposed STM network outperforms the state-of-the-art methods on both temporal-related datasets (i.e., Something-Something v1 & v2 and Jester) and scene-related datasets (i.e., Kinetics-400, UCF-101, and HMDB-51) with the help of encoding spatiotemporal and motion features together. * The work was done during an internship at SenseTime.

Convolutional Neural Networks (CNN) have been regarded as a powerful class of models for image recognition problems. Nevertheless, it is not trivial when utilizing a CNN for learning spatio-temporal video representation. A few studies have shown that performing 3D convolutions is a rewarding approach to capture both spatial and temporal dimensions in videos. However, the development of a very deep 3D CNN from scratch results in expensive computational cost and memory demand. A valid question is why not recycle off-the-shelf 2D networks for a 3D CNN. In this paper, we devise multiple variants of bottleneck building blocks in a residual learning framework by simulating 3 × 3 × 3 convolutions with 1 × 3 × 3 convolutional filters on spatial domain (equivalent to 2D CNN) plus 3 × 1 × 1 convolutions to construct temporal connections on adjacent feature maps in time. Furthermore, we propose a new architecture, named , that exploits all the variants of blocks but composes each in different placement of ResNet, following the philosophy that enhancing structural diversity with going deep could improve the power of neural networks. Our P3D ResNet achieves clear improvements on Sports-1M video classification dataset against 3D CNN and frame-based 2D CNN by 5.3% and 1.8%, respectively. We further examine the generalization performance of video representation produced by our pre-trained P3D ResNet on five different benchmarks and three different tasks, demonstrating superior performances over several state-of-the-art techniques.

Temporal modeling is key for action recognition in videos. It normally considers both short-range motions and long-range aggregations. In this paper, we propose a Temporal Excitation and Aggregation (TEA) block, including a motion excitation (ME) module and a multiple temporal aggregation (MTA) module, specifically designed to capture both short-and long-range temporal evolution. In particular, for short-range motion modeling, the ME module calculates the feature-level temporal differences from spatiotemporal features. It then utilizes the differences to excite the motion-sensitive channels of the features. The long-range temporal aggregations in previous works are typically achieved by stacking a large number of local temporal convolutions. Each convolution processes a local temporal window at a time. In contrast, the MTA module proposes to deform the local convolution to a group of subconvolutions, forming a hierarchical residual architecture. Without introducing additional parameters, the features will be processed with a series of sub-convolutions, and each frame could complete multiple temporal aggregations with neighborhoods. The final equivalent receptive field of temporal dimension is accordingly enlarged, which is capable of modeling the long-range temporal relationship over distant frames. The two components of the TEA block are complementary in temporal modeling. Finally, our approach achieves impressive results at low FLOPs on several action recognition benchmarks, such as Kinetics, Something-Something, HMDB51, and UCF101, which confirms its effectiveness and efficiency.

高效的时空建模是视频动作识别的重要而挑战性问题。现有的最先进的方法利用相邻的特征差异，以获得短期时间建模的运动线索，简单的卷积。然而，只有一个本地卷积，由于接收领域有限而无法处理各种动作。此外，摄像机运动带来的动作耳鸣还将损害提取的运动功能的质量。在本文中，我们提出了一个时间显着积分（TSI）块，其主要包含突出运动激励（SME）模块和交叉感知时间集成（CTI）模块。具体地，中小企业旨在通过空间级局部 - 全局运动建模突出显示运动敏感区域，其中显着对准和金字塔型运动建模在相邻帧之间连续进行，以捕获由未对准背景引起的噪声较少的运动动态。 CTI旨在分别通过一组单独的1D卷积进行多感知时间建模。同时，不同看法的时间相互作用与注意机制相结合。通过这两个模块，通过引入有限的附加参数，可以有效地编码长短的短期时间关系。在几个流行的基准测试中进行了广泛的实验（即，某种东西 - 某种东西 - 东西 - 400，uCF-101和HMDB-51），这证明了我们所提出的方法的有效性。

Temporal action detection (TAD) is extensively studied in the video understanding community by generally following the object detection pipeline in images. However, complex designs are not uncommon in TAD, such as two-stream feature extraction, multi-stage training, complex temporal modeling, and global context fusion. In this paper, we do not aim to introduce any novel technique for TAD. Instead, we study a simple, straightforward, yet must-known baseline given the current status of complex design and low detection efficiency in TAD. In our simple baseline (termed BasicTAD), we decompose the TAD pipeline into several essential components: data sampling, backbone design, neck construction, and detection head. We extensively investigate the existing techniques in each component for this baseline, and more importantly, perform end-to-end training over the entire pipeline thanks to the simplicity of design. As a result, this simple BasicTAD yields an astounding and real-time RGB-Only baseline very close to the state-of-the-art methods with two-stream inputs. In addition, we further improve the BasicTAD by preserving more temporal and spatial information in network representation (termed as PlusTAD). Empirical results demonstrate that our PlusTAD is very efficient and significantly outperforms the previous methods on the datasets of THUMOS14 and FineAction. Meanwhile, we also perform in-depth visualization and error analysis on our proposed method and try to provide more insights on the TAD problem. Our approach can serve as a strong baseline for future TAD research. The code and model will be released at https://github.com/MCG-NJU/BasicTAD.

步态识别能够实现长途且无接触式识别，是一项重要的生物识别技术。最近的步态识别方法着重于学习步行过程中人类运动或外观的模式，并构建相应的时空表示。但是，不同的个体有自己的运动模式定律，简单的时空特征很难描述人类部位运动的变化，尤其是当包括服装和携带等混淆变量时，可以降低特征的区分性。在本文中，我们提出了运动激发模块（MEM）来指导时空特征，以专注于具有较大动态变化的人类部位，MEM了解框架和间隔之间的差异信息，以获得时间运动变化的表示形式，值得一提的是，MEM可以适应不确定长度的帧序列，并且不会添加任何其他参数。此外，我们提出了精细的提取器（FFE），该提取器（FFE）独立地根据个体的不同水平部分学习人体的时空表示。受益于MEM和FFE，我们的方法创新地结合了运动变化信息，从而显着改善了在跨外观条件下模型的性能。在流行的数据集Casia-B上，我们提出的运动步态比现有的步态识别方法更好。

Human activity recognition (HAR) using drone-mounted cameras has attracted considerable interest from the computer vision research community in recent years. A robust and efficient HAR system has a pivotal role in fields like video surveillance, crowd behavior analysis, sports analysis, and human-computer interaction. What makes it challenging are the complex poses, understanding different viewpoints, and the environmental scenarios where the action is taking place. To address such complexities, in this paper, we propose a novel Sparse Weighted Temporal Attention (SWTA) module to utilize sparsely sampled video frames for obtaining global weighted temporal attention. The proposed SWTA is comprised of two parts. First, temporal segment network that sparsely samples a given set of frames. Second, weighted temporal attention, which incorporates a fusion of attention maps derived from optical flow, with raw RGB images. This is followed by a basenet network, which comprises a convolutional neural network (CNN) module along with fully connected layers that provide us with activity recognition. The SWTA network can be used as a plug-in module to the existing deep CNN architectures, for optimizing them to learn temporal information by eliminating the need for a separate temporal stream. It has been evaluated on three publicly available benchmark datasets, namely Okutama, MOD20, and Drone-Action. The proposed model has received an accuracy of 72.76%, 92.56%, and 78.86% on the respective datasets thereby surpassing the previous state-of-the-art performances by a margin of 25.26%, 18.56%, and 2.94%, respectively.

现有的基于3D骨架的动作识别方法通过将手工制作的动作功能编码为图像格式和CNN解码，从而达到了令人印象深刻的性能。但是，这种方法在两种方面受到限制：a）手工制作的动作功能很难处理具有挑战性的动作，b）通常需要复杂的CNN模型来提高动作识别精度，这通常会发生重大计算负担。为了克服这些局限性，我们引入了一种新颖的AFE-CNN，它致力于增强基于3D骨架的动作的特征，以适应具有挑战性的动作。我们提出了功能增强从关键关节，骨向量，关键框架和时间视角的模块，因此，AFE-CNN对摄像头视图和车身大小变化更为强大，并显着提高了对挑战性动作的识别精度。此外，我们的AFE-CNN采用了轻巧的CNN模型以增强动作功能来解码图像，从而确保了比最新方法低得多的计算负担。我们在三个基于基准骨架的动作数据集上评估了AFE-CNN：NTU RGB+D，NTU RGB+D 120和UTKINECT-ACTION3D，并取得了广泛的实验结果，这表明我们对AFE-CNN的出色表现。

未来的活动预期是在Egocentric视觉中具有挑战性问题。作为标准的未来活动预期范式，递归序列预测遭受错误的累积。为了解决这个问题，我们提出了一个简单有效的自我监管的学习框架，旨在使中间表现为连续调节中间代表性，以产生表示（a）与先前观察到的对比的当前时间戳框架中的新颖信息内容和（b）反映其与先前观察到的帧的相关性。前者通过最小化对比损失来实现，并且后者可以通过动态重量机制来实现在观察到的内容中的信息帧中，具有当前帧的特征与观察到的帧之间的相似性比较。通过多任务学习可以进一步增强学习的最终视频表示，该多任务学习在目标活动标签上执行联合特征学习和自动检测到的动作和对象类令牌。在大多数自我传统视频数据集和两个第三人称视频数据集中，SRL在大多数情况下急剧表现出现有的现有最先进。通过实验性事实，还可以准确识别支持活动语义的行动和对象概念的实验性。

设计可以成功部署在日常生活环境中的活动检测系统需要构成现实情况典型挑战的数据集。在本文中，我们介绍了一个新的未修剪日常生存数据集，该数据集具有几个现实世界中的挑战：Toyota Smarthome Untrimmed（TSU）。 TSU包含以自发方式进行的各种活动。数据集包含密集的注释，包括基本的，复合活动和涉及与对象相互作用的活动。我们提供了对数据集所需的现实世界挑战的分析，突出了检测算法的开放问题。我们表明，当前的最新方法无法在TSU数据集上实现令人满意的性能。因此，我们提出了一种新的基线方法，以应对数据集提供的新挑战。此方法利用一种模态（即视线流）生成注意力权重，以指导另一种模态（即RGB）以更好地检测活动边界。这对于检测以高时间差异为特征的活动特别有益。我们表明，我们建议在TSU和另一个受欢迎的挑战数据集Charades上优于最先进方法的方法。

动作检测的任务旨在在每个动作实例中同时推论动作类别和终点的本地化。尽管Vision Transformers推动了视频理解的最新进展，但由于在长时间的视频剪辑中，设计有效的架构以进行动作检测是不平凡的。为此，我们提出了一个有效的层次时空时空金字塔变压器（STPT）进行动作检测，这是基于以下事实：变压器中早期的自我注意力层仍然集中在局部模式上。具体而言，我们建议在早期阶段使用本地窗口注意来编码丰富的局部时空时空表示，同时应用全局注意模块以捕获后期的长期时空依赖性。通过这种方式，我们的STPT可以用冗余的大大减少来编码区域和依赖性，从而在准确性和效率之间进行有希望的权衡。例如，仅使用RGB输入，提议的STPT在Thumos14上获得了53.6％的地图，超过10％的I3D+AFSD RGB模型超过10％，并且对使用其他流量的额外流动功能的表现较少，该流量具有31％的GFLOPS ，它是一个有效，有效的端到端变压器框架，用于操作检测。

行动预测旨在通过部分观察视频推断即将举行的人类行动，这是由于早期观察结果有限的信息有限。现有方法主要采用重建策略来处理此任务，期望从部分观察到完整视频来学习单个映射函数，以便于预测过程。在这项研究中，我们提出了来自两个新方面的部分视频查询生成“完整视频”功能调节的对抗性记忆网络（AMEMNet）。首先，键值结构化存储器发生器旨在将不同的部分视频存储为键存储器，并在具有门控机制和查询关注的值存储器中动态地写入完整视频。其次，我们开发了一个类感知判别者，以指导内存发生器在对抗训练时不仅提供现实，而且还提供鉴别的完整视频特征。通过RGB和光学流量的晚期融合给出了AMEMNET的最终预测结果。提供两个基准视频数据集，UCF-101和HMDB51的广泛实验结果，以证明所提出的AMEMNET模型在最先进的方法的有效性。

人类行动识别是计算机视觉中的重要应用领域。它的主要目的是准确地描述人类的行为及其相互作用，从传感器获得的先前看不见的数据序列中。识别，理解和预测复杂人类行动的能力能够构建许多重要的应用，例如智能监视系统，人力计算机界面，医疗保健，安全和军事应用。近年来，计算机视觉社区特别关注深度学习。本文使用深度学习技术的视频分析概述了当前的动作识别最新识别。我们提出了识别人类行为的最重要的深度学习模型，并分析它们，以提供用于解决人类行动识别问题的深度学习算法的当前进展，以突出其优势和缺点。基于文献中报道的识别精度的定量分析，我们的研究确定了动作识别中最新的深层体系结构，然后为该领域的未来工作提供当前的趋势和开放问题。

人类对象相互作用（HOI）识别的关键是推断人与物体之间的关系。最近，该图像的人类对象相互作用（HOI）检测取得了重大进展。但是，仍然有改善视频HOI检测性能的空间。现有的一阶段方法使用精心设计的端到端网络来检测视频段并直接预测交互。它使网络的模型学习和进一步的优化更加复杂。本文介绍了空间解析和动态时间池（SPDTP）网络，该网络将整个视频作为时空图作为人类和对象节点作为输入。与现有方法不同，我们提出的网络通过显式空间解析预测交互式和非相互作用对之间的差异，然后执行交互识别。此外，我们提出了一个可学习且可区分的动态时间模块（DTM），以强调视频的关键帧并抑制冗余帧。此外，实验结果表明，SPDTP可以更多地关注主动的人类对象对和有效的密钥帧。总体而言，我们在CAD-1220数据集和某些ELSE数据集上实现了最先进的性能。

在视频数据中，来自移动区域的忙碌运动细节在频域中的特定频率带宽内传送。同时，视频数据的其余频率是用具有实质冗余的安静信息编码，这导致现有视频模型中的低处理效率作为输入原始RGB帧。在本文中，我们考虑为处理重要忙碌信息的处理和对安静信息的计算的处理分配。我们设计可训练的运动带通量模块（MBPM），用于将繁忙信息从RAW视频数据中的安静信息分开。通过将MBPM嵌入到两个路径CNN架构中，我们定义了一个繁忙的网络（BQN）。 BQN的效率是通过避免由两个路径处理的特征空间中的冗余来确定：一个在低分辨率的安静特征上运行，而另一个处理繁忙功能。所提出的BQN在某物V1，Kinetics400，UCF101和HMDB51数据集中略高于最近最近的视频处理模型。