Papers Skeleton Based Action Recognition
“Skeleton Based Action Recognition” 태그가 달린 논문 419편 · 필터 해제
Zero-shot Skeleton-based Action Recognition with Prototype-guided Feature Alignment
Zero-shot skeleton-based action recognition aims to classify unseen skeleton-based human actions without prior exposure to such categories during training. This task is extremely challenging due to the difficulty in gene…
Action RecognitionOne-Shot 3D Action RecognitionSkeleton Based Action RecognitionTransfer Learning+2Including Semantic Information via Word Embeddings for Skeleton-based Action Recognition
Effective human action recognition is widely used for cobots in Industry 4.0 to assist in assembly tasks. However, conventional skeleton-based methods often lose keypoint semantics, limiting their effectiveness in comple…
Action RecognitionSkeleton Based Action RecognitionTemporal Action LocalizationWord Embeddings3D Skeleton-Based Action Recognition: A Review
With the inherent advantages of skeleton representation, 3D skeleton-based action recognition has become a prominent topic in the field of computer vision. However, previous reviews have predominantly adopted a model-ori…
Action RecognitionData AugmentationMambaSkeleton Based Action RecognitionSpatio-Temporal Joint Density Driven Learning for Skeleton-Based Action Recognition
Traditional approaches in unsupervised or self supervised learning for skeleton-based action classification have concentrated predominantly on the dynamic aspects of skeletal sequences. Yet, the intricate interaction bet…
Action ClassificationAction RecognitionContrastive LearningSelf-Supervised Learning+1SkeletonX: Data-Efficient Skeleton-based Action Recognition via Cross-sample Feature Aggregation
While current skeleton action recognition models demonstrate impressive performance on large-scale datasets, their adaptation to new application scenarios remains challenging. These challenges are particularly pronounced…
Action RecognitionOne-Shot 3D Action RecognitionSkeleton Based Action RecognitionAction Recognition in Real-World Ambient Assisted Living Environment
The growing ageing population and their preference to maintain independence by living in their own homes require proactive strategies to ensure safety and support. Ambient Assisted Living (AAL) technologies have emerged …
Action RecognitionComputational EfficiencyData AugmentationHand Gesture Recognition+1Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language Recognition
Sign language recognition (SLR) refers to interpreting sign language glosses from given videos automatically. This research area presents a complex challenge in computer vision because of the rapid and intricate movement…
Action RecognitionComputational EfficiencySign Language RecognitionSkeleton Based Action RecognitionBST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports
Badminton, known for having the fastest ball speeds among all sports, presents significant challenges to the field of computer vision, including player identification, court line detection, shuttlecock trajectory trackin…
Action RecognitionLine DetectionPose EstimationSkeleton Based Action Recognition+2HyLiFormer: Hyperbolic Linear Attention for Skeleton-based Human Action Recognition
Transformers have demonstrated remarkable performance in skeleton-based human action recognition, yet their quadratic computational complexity remains a bottleneck for real-world applications. To mitigate this, linear at…
Action RecognitionSkeleton Based Action RecognitionTemporal Action LocalizationBlanketGen2-Fit3D: Synthetic Blanket Augmentation Towards Improving Real-World In-Bed Blanket Occluded Human Pose Estimation
Human Pose Estimation (HPE) from monocular RGB images is crucial for clinical in-bed skeleton-based action recognition, however, it poses unique challenges for HPE models due to the frequent presence of blankets occludin…
Action RecognitionData AugmentationPose EstimationSkeleton Based Action RecognitionDSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology Modeling
Graph convolutional networks (GCNs) have emerged as a powerful tool for skeleton-based action and gesture recognition, thanks to their ability to model spatial and temporal dependencies in skeleton data. However, existin…
Action RecognitionGesture RecognitionHand Gesture RecognitionSkeleton Based Action RecognitionHFGCN:Hypergraph Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition
In recent years, action recognition has received much attention and wide application due to its important role in video understanding. Most of the researches on action recognition methods focused on improving the perform…
Action RecognitionRelation ClassificationSkeleton Based Action RecognitionVideo UnderstandingIoT-Based Real-Time Medical-Related Human Activity Recognition Using Skeletons and Multi-Stage Deep Learning for Healthcare
The Internet of Things (IoT) and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients. Recognizing medical-related human activities (MRHA) is pivotal for …
Activity RecognitionHuman Activity RecognitionSkeleton Based Action RecognitionImproving Skeleton-based Action Recognition with Interactive Object Information
Human skeleton information is important in skeleton-based action recognition, which provides a simple and efficient way to describe human pose. However, existing skeleton-based methods focus more on the skeleton, ignorin…
Action RecognitionData Augmentationgraph constructionObject+1Evolving Skeletons: Motion Dynamics in Action Recognition
Skeleton-based action recognition has gained significant attention for its ability to efficiently represent spatiotemporal information in a lightweight format. Most existing approaches use graph-based models to process s…
Action RecognitionHypergraph representationsSkeleton Based Action RecognitionSemantic-guided Cross-Modal Prompt Learning for Skeleton-based Zero-shot Action Recognition
Skeleton-based human action recognition is promising due to its privacy preservation, robustness to visual challenges, and computational efficiency. Especially, the practical necessity to recognize unseen actions has…
Action RecognitionComputational EfficiencyPrompt LearningSkeleton Based Action Recognition+4Skeleton-based Action Recognition with Non-linear Dependency Modeling and Hilbert-Schmidt Independence Criterion
Human skeleton-based action recognition has long been an indispensable aspect of artificial intelligence. Current state-of-the-art methods tend to consider only the dependencies between connected skeletal joints, limitin…
Action RecognitionSkeleton Based Action RecognitionHierarchical Temporal Convolution Network:Towards Privacy-Centric Activity Recognition
In response to the healthcare issues associated with the ageing population, various ambient assisted living technologies are being developed. To mitigate privacy concerns related to cloud-based data processing, recent me…
Activity RecognitionSkeleton Based Action RecognitionSynchronized and Fine-Grained Head for Skeleton-Based Ambiguous Action Recognition
Skeleton-based action recognition using GCNs has achieved remarkable performance, but recognizing ambiguous actions, such as "waving" and "saluting", remains a significant challenge. Existing methods typically rely on a …
Action RecognitionSkeleton Based Action RecognitionMSA-GCN: Exploiting Multi-Scale Temporal Dynamics With Adaptive Graph Convolution for Skeleton-Based Action Recognition
Graph convolutional networks (GCNs) have been widely used and have achieved remarkable results in skeleton-based action recognition. We note that existing GCN-based approaches rely on local context information of the ske…
Action RecognitionSkeleton Based Action Recognition