Skeleton Aware Multi-modal Sign Language Recognition
Northeastern University（东北大学）
Songyao Jiang, Bin Sun, Lichen Wang, Yue Bai, Kunpeng Li, Yun Fu
备注：
This is a preprint version of our work SAM-SLR that ranked 1st at CVPR2021 Challenge on Large Scale Signer Independent Isolated Sign Language Recognition
Sign language is commonly used by deaf or speech impaired people to communicate but requires significant effort to master. Sign Language Recognition (SLR) aims to bridge the gap between sign language users and others by recognizing signs from given videos. It is an essential yet challenging task since sign language is performed with the fast and complex movement of hand gestures, body posture, and even facial expressions. Recently, skeleton-based action recognition attracts increasing attention due to the independence between the subject and background variation. However, skeleton-based SLR is still under exploration due to the lack of annotations on hand keypoints. Some efforts have been made to use hand detectors with pose estimators to extract hand key points and learn to recognize sign language via Neural Networks, but none of them outperforms RGB-based methods. To this end, we propose a novel Skeleton Aware Multi-modal SLR framework (SAM-SLR) to take advantage of multi-modal information towards a higher recognition rate. Specifically, we propose a Sign Language Graph Convolution Network (SL-GCN) to model the embedded dynamics and a novel Separable Spatial-Temporal Convolution Network (SSTCN) to exploit skeleton features. RGB and depth modalities are also incorporated and assembled into our framework to provide global information that is complementary to the skeleton-based methods SL-GCN and SSTCN. As a result, SAM-SLR achieves the highest performance in both RGB (98.42\%) and RGB-D (98.53\%) tracks in 2021 Looking at People Large Scale Signer Independent Isolated SLR Challenge. Our code is available at https://github.com/jackyjsy/CVPR21Chal-SLR
Shanghai Jiao Tong University（上海交通大学）；Illinois Institute of Technology（伊利诺伊理工学院）
Action recognition has been a heated topic in computer vision for its wide application in vision systems. Previous approaches achieve improvement by fusing the modalities of the skeleton sequence and RGB video. However, such methods have a dilemma between the accuracy and efficiency for the high complexity of the RGB video network. To solve the problem, we propose a multi-modality feature fusion network to combine the modalities of the skeleton sequence and RGB frame instead of the RGB video, as the key information contained by the combination of skeleton sequence and RGB frame is close to tha…
Multi-modal human action analysis is a critical and attractive research topic. However, the majority of the existing datasets only provide visual modalities (i.e., RGB, depth and skeleton). To make up this, we introduce a new, large-scale EV-Action dataset in this work, which consists of RGB, depth, electromyography (EMG), and two skeleton modalities. Compared with the conventional datasets, EV-Action dataset has two major improvements: (1) we deploy a motion capturing system to obtain high quality skeleton modality, which provides more comprehensive motion information including skeleton, traj…
Multi-Modality Co-Learning for Efficient Skeleton-based Action Recognition
State Key Laboratory of General Artificial Intelligence, Peking University（通用人工智能国家重点实验室，北京大学）；Center for Research in Computer Vision, University of Central Florida（中佛罗里达大学计算机视觉研究中心）
Cross-Modal Action Recognition in Egocentric Video Using Mamba: Integrating RGB and Hand Skeleton Streams via CLS Token Fusion Strategies
Univ. of Buenos Aires. Faculty of Exact and Natural Sciences. Dept. of Computer Science (DC)（布宜诺斯艾利斯大学。精确与自然科学学院。计算机科学系）；CONICET-Univ. of Buenos Aires. Institute of Computer Sciences (ICC)（布宜诺斯艾利斯大学CONICET联合体。计算机科学研究所）
The Chinese University of HongKong（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；SenseTime Research（商汤科技研究院）；Nanyang Technological University（南洋理工大学）
Human skeleton, as a compact representation of human action, has received increasing attention in recent years. Many skeleton-based action recognition methods adopt graph convolutional networks (GCN) to extract features on top of human skeletons. Despite the positive results shown in previous works, GCN-based methods are subject to limitations in robustness, interoperability, and scalability. In this work, we propose PoseC3D, a new approach to skeleton-based action recognition, which relies on a 3D heatmap stack instead of a graph sequence as the base representation of human skeletons. Compare…
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机技术研究所）
With the prevalence of RGB-D cameras, multi-modal video data have become more available for human action recognition. One main challenge for this task lies in how to effectively leverage their complementary information. In this work, we propose a Modality Compensation Network (MCN) to explore the relationships of different modalities, and boost the representations for human action recognition. We regard RGB/optical flow videos as source modalities, skeletons as auxiliary modality. Our goal is to extract more discriminative features from source modalities, with the help of auxiliary modality. B…
Skeleton-to-Image Encoding: Enabling Skeleton Representation Learning via Vision-Pretrained Models
KTH Royal Institute of Technology（皇家理工学院）；Lancaster University（兰卡斯特大学）；Hebei University of Technology（河北工业大学）；Nanyang Technological University（南洋理工大学）；Shenzhen MSU-BIT University（深圳MSU-BIT大学）；VinUniversity（文大学）
Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-Supervision
Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院人形机器人与机器人研究所）；Institute for Artificial Intelligence, University of Stuttgart（斯图加特大学人工智能研究所）；School of Robotics, Hunan University（湖南大学机器人学院）
Zero-Shot Skeleton-based Action Recognition with Dual Visual-Text Alignment
School of Cyber Science and Engineering, Southeast University, Nanjing, China（东南大学计算机科学与工程学院）；Engineering Research Center of Blockchain Application, Supervision And Management (Southeast University), Ministry of Education, China（区块链应用工程研究中心（东南大学））；School of Computer Science and Engineering, Southeast University, Nanjing, China（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（新一代人工智能技术及其交叉应用关键实验室（东南大学））；School of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China（深圳大学计算机科学与软件工程学院）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen 518060, China（大数据系统计算技术国家工程实验室（深圳大学））；Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China（广东省智能信息处理重点实验室（深圳大学））；Purple Mountain Laboratories, Nanjing 210000, China（紫金山实验室）
Sign language is commonly used by deaf or mute people to communicate but requires extensive effort to master. It is usually performed with the fast yet delicate movement of hand gestures, body posture, and even facial expressions. Current Sign Language Recognition (SLR) methods usually extract features via deep neural networks and suffer overfitting due to limited and noisy data. Recently, skeleton-based action recognition has attracted increasing attention due to its subject-invariant and background-invariant nature, whereas skeleton-based SLR is still under exploration due to the lack of han…
Toward Universal Skeleton-Based Action Recognition across Heterogeneous Skeletons and Open Vocabularies
School of Cyber Science and Engineering, Southeast University, Nanjing, China（东南大学计算机科学与工程学院，南京，中国）；School of Computer Science and Engineering, Southeast University, Nanjing, China（东南大学计算机科学与工程学院，南京，中国）
School of Computer Science and Engineering, Southeast University, Nanjing 210096, China（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（新一代人工智能技术及交叉应用关键实验室（东南大学））；School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China（东南大学网络科学与工程学院）；Engineering Research Center of Blockchain Application, Supervision And Management (Southeast University), Ministry of Education, China（区块链应用、监督与管理工程研究中心（东南大学））；Purple Mountain Laboratories, Nanjing 210000, China（紫金山实验室）
In this paper, we present Fusion-GCN, an approach for multimodal action recognition using Graph Convolutional Networks (GCNs). Action recognition methods based around GCNs recently yielded state-of-the-art performance for skeleton-based action recognition. With Fusion-GCN, we propose to integrate various sensor data modalities into a graph that is trained using a GCN model for multi-modal action recognition. Additional sensor measurements are incorporated into the graph representation, either on a channel dimension (introducing additional node attributes) or spatial dimension (introducing new …
Beihang University（北京航空航天大学）；Zhongguancun Laboratory（中关村实验室）；Hangzhou Innovation Institute of Beihang University（北京航空航天大学杭州创新研究院）
Human action recognition aims at classifying the category of human action from a segment of a video. Recently, people have dived into designing GCN-based models to extract features from skeletons for performing this task, because skeleton representations are much more efficient and robust than other modalities such as RGB frames. However, when employing the skeleton data, some important clues like related items are also discarded. It results in some ambiguous actions that are hard to be distinguished and tend to be misclassified. To alleviate this problem, we propose an auxiliary feature refin…
Graph convolutional networks (GCNs), which can model the human body skeletons as spatial and temporal graphs, have shown remarkable potential in skeleton-based action recognition. However, in the existing GCN-based methods, graph-structured representation of the human skeleton makes it difficult to be fused with other modalities, especially in the early stages. This may limit their scalability and performance in action recognition tasks. In addition, the pose information, which naturally contains informative and discriminative clues for action recognition, is rarely explored together with skel…
This paper strives for action recognition and detection in video modalities like RGB, depth maps or 3D-skeleton sequences when only limited modality-specific labeled examples are available. For the RGB, and derived optical-flow, modality many large-scale labeled datasets have been made available. They have become the de facto pre-training choice when recognizing or detecting new actions from RGB datasets that have limited amounts of labeled examples available. Unfortunately, large-scale labeled action datasets for other modalities are unavailable for pre-training. In this paper, our goal is to…
Navigating Open Set Scenarios for Skeleton-based Action Recognition
School of Robotics, Hunan University（湖南大学机器人学院）；Mercedes-Benz Tech Innovation（梅赛德斯-奔驰技术创新公司）；Institute for Artificial Intelligence, University of Stuttgart（斯图加特大学人工智能研究所）
HDBN: A Novel Hybrid Dual-branch Network for Robust Skeleton-based Action Recognition
School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能工程学院）；National Key Laboratory of General Artificial Intelligence, Peking University（北京大学通用人工智能重点实验室）
Institute of Computer Science and Technology, Peking University（北京大学计算机科学技术研究所）
With the prevalence of accessible depth sensors, dynamic human body skeletons have attracted much attention as a robust modality for action recognition. Previous methods model skeletons based on RNN or CNN, which has limited expressive power for irregular skeleton joints. While graph convolutional networks (GCN) have been proposed to address irregular graph-structured data, the fundamental graph construction remains challenging. In this paper, we represent skeletons naturally on graphs, and propose a graph regression based GCN (GR-GCN) for skeleton-based action recognition, aiming to capture t…
Hikvision Research Institute（海康威视研究院）；College of Information Science & Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）
Graph Convolutional Networks (GCNs) have attracted increasing interests for the task of skeleton-based action recognition. The key lies in the design of the graph structure, which encodes skeleton topology information. In this paper, we propose Dynamic GCN, in which a novel convolutional neural network named Contextencoding Network (CeN) is introduced to learn skeleton topology automatically. In particular, when learning the dependency between two joints, contextual features from the rest joints are incorporated in a global manner. CeN is extremely lightweight yet effective, and can be embedde…
University at Buffalo, State University of New York（纽约州立大学布法罗分校）；Mitsubishi Electric Research Laboratories（三菱电机研究实验室）
Graph Convolutional Networks (GCNs) have been widely used to model the high-order dynamic dependencies for skeleton-based action recognition. Most existing approaches do not explicitly embed the high-order spatio-temporal importance to joints' spatial connection topology and intensity, and they do not have direct objectives on their attention module to jointly learn when and where to focus on in the action sequence. To address these problems, we propose the To-a-T Spatio-Temporal Focus (STF), a skeleton-based action recognition framework that utilizes the spatio-temporal gradient to focus on r…
Center for Visual Information Technology（视觉信息技术中心）；IIIT Hyderabad（海得拉巴国际信息技术学院）
We introduce SynSE, a novel syntactically guided generative approach for Zero-Shot Learning (ZSL). Our end-to-end approach learns progressively refined generative embedding spaces constrained within and across the involved modalities (visual, language). The inter-modal constraints are defined between action sequence embedding and embeddings of Parts of Speech (PoS) tagged words in the corresponding action description. We deploy SynSE for the task of skeleton-based action sequence recognition. Our design choices enable SynSE to generalize compositionally, i.e., recognize sequences whose action …
State Key Laboratory of General Artificial Intelligence, Peking University, Shenzhen Graduate School（国家一般人工智能实验室，北京大学深圳研究生院）；Department of Information Engineering and Computer Science (DISI), University of Trento（信息工程与计算机科学系（DISI），特伦托大学）；University at Buffalo, The State University of New York（布法罗大学，纽约州立大学）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）
Nanyang Technological University（南洋理工大学）；Alibaba Group（阿里巴巴集团）；University of Sydney（悉尼大学）
Skeleton-based human action recognition has attracted a lot of research attention during the past few years. Recent works attempted to utilize recurrent neural networks to model the temporal dependencies between the 3D positional configurations of human body joints for better analysis of human activities in the skeletal data. The proposed work extends this idea to spatial domain as well as temporal domain to better analyze the hidden sources of action-related information within the human skeleton sequences in both of these domains simultaneously. Based on the pictorial structure of Kinect's sk…
The aim of this work is to contribute to the development of a tactile device for visually impaired and blind persons in order to let them to understand actions of the surrounding people and to interact with them. First, based on the state-of-the-art methods of human action recognition from RGB-D sequences, we use the skeleton information provided by Kinect, with the disentangled and unified multi-scale Graph Convolutional (MS-G3D) model to recognize the performed actions. We tested this model on real scenes and found some of constraints and limitations. Next, we apply a fusion between skeleton…
Zero-Shot Skeleton-Based Action Recognition With Prototype-Guided Feature Alignment
School of Software Engineering, South China University of Technology（南方科技大学软件工程学院）；South China University of Technology（南方科技大学）；Pazhou Lab（琶洲实验室）；School of Future Technology, South China University of Technology（未来技术学院）；Peng Cheng Laboratory（鹏城实验室）；Key Laboratory of Big Data and Intelligent Robot (South China University of Technology), Ministry of Education（大数据与智能机器人重点实验室）
Marrying Text-to-Motion Generation with Skeleton-Based Action Recognition
School of Cyber Science and Engineering, Southeast University, Nanjing, China（东南大学计算机科学与工程学院，南京，中国）；School of Computer Science and Engineering, Southeast University, Nanjing, China（东南大学计算机科学与工程学院，南京，中国）
T-MOR: Learning Motion-Aware Skeleton Representations for Human Action Recognition
Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；Inria Center at Université Côte d'Azur（法国蔚蓝海岸大学Inria中心）；Woven by Toyota；Toyota Motor Europe（丰田汽车欧洲公司）
R&D Center of Artificial Intelligent, NUCTECH Company Limited（NUCTECH有限公司人工智能研发中心）；Department of Engineering Physics, Tsinghua University（清华大学工程物理系）；Informatics Institute, University of Amsterdam（阿姆斯特丹大学信息学院）；Department of Computer Science and Information Systems, Birkbeck College（伦敦大学伯贝克学院计算机科学与信息系统系）
Skeleton-based action recognition has attracted considerable attention in computer vision since skeleton data is more robust to the dynamic circumstance and complicated background than other modalities. Recently, many researchers have used the Graph Convolutional Network (GCN) to model spatial-temporal features of skeleton sequences by an end-to-end optimization. However, conventional GCNs are feedforward networks which are impossible for low-level layers to access semantic information in the high-level layers. In this paper, we propose a novel network, named Feedback Graph Convolutional Netwo…
Tianjin University（天津大学）；Key Research Center for Surface Monitoring and Analysis of Cultural Relics (SMARC), State Administration of Cultural Heritage, China（中国国家文物局文物表面监测与分析重点研究中心）；University of South Carolina（南卡罗来纳大学）
Skeleton-based human action recognition has been drawing more interest recently due to its low sensitivity to appearance changes and the accessibility of more skeleton data. However, even the 3D skeletons captured in practice are still sensitive to the viewpoint and direction gave the occlusion of different human-body joints and the errors in human joint localization. Such view variance of skeleton data may significantly affect the performance of action recognition. To address this issue, we propose in this paper a new view-invariant representation learning approach, without any manual action …
Singapore University of Technology and Design（新加坡科技设计大学）；The University of Melbourne（墨尔本大学）；Lancaster University（兰卡斯特大学）；The University of Western Australia（西澳大学）；Alibaba Group（阿里巴巴集团）
Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action. It has a wide range of applications, and therefore has been attracting increasing attention in the field of computer vision. Human actions can be represented using various data modalities, such as RGB, skeleton, depth, infrared, point cloud, event stream, audio, acceleration, radar, and WiFi signal, which encode different sources of useful yet distinct information and have various advantages depending on the application scenarios. Consequently, lots of existing works have attempted to investigat…
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 st…
Human action recognition has been widely used in many fields of life, and many human action datasets have been published at the same time. However, most of the multi-modal databases have some shortcomings in the layout and number of sensors, which cannot fully represent the action features. Regarding the problems, this paper proposes a freely available dataset, named CZU-MHAD (Changzhou University: a comprehensive multi-modal human action dataset). It consists of 22 actions and three modals temporal synchronized data. These modals include depth videos and skeleton positions from a kinect v2 ca…
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Shandong University of Science and Technology（山东科技大学）；Artificial Intelligence Research, Chinese Academy of Sciences（中国科学院人工智能研究院）
Current methods for skeleton-based human action recognition usually work with complete skeletons. However, in real scenarios, it is inevitable to capture incomplete or noisy skeletons, which could significantly deteriorate the performance of current methods when some informative joints are occluded or disturbed. To improve the robustness of action recognition models, a multi-stream graph convolutional network (GCN) is proposed to explore sufficient discriminative features spreading over all skeleton joints, so that the distributed redundant representation reduces the sensitivity of the action …
Balanced Representation Learning for Long-tailed Skeleton-based Action Recognition
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；University of Science and Technology of China（中国科学技术大学）
