https://github.com/lshiwjx/DSTA-Net
공식 코드Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action Recognition
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Temporal Action Localization aims to detect activities in the video stream and output beginning and end timestamps. It is closely related to Temporal Action Proposal Generation.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action Recognition
Enhancing Temporal Action Localization: Advanced S6 Modeling with Recurrent Mechanism
Decomposing and Editing Predictions by Modeling Model Computation
CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation
Masked Motion Predictors are Strong 3D Action Representation Learners
Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data
DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity Recognition
Temporal Action Localization for Inertial-based Human Activity Recognition
WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition
Generative Action Description Prompts for Skeleton-based Action Recognition
Generative Action Description Prompts for Skeleton-based Action Recognition
BasicTAD: an Astounding RGB-Only Baseline for Temporal Action Detection
MGSampler: An Explainable Sampling Strategy for Video Action Recognition
PointTAD: Multi-Label Temporal Action Detection with Learnable Query Points
Evaluating Dependencies in Fact Editing for Language Models: Specificity and Implication Awareness
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Fusion-GCN: Multimodal Action Recognition using Graph Convolutional Networks
MotionAug: Augmentation with Physical Correction for Human Motion Prediction
Auxiliary Tasks Benefit 3D Skeleton-based Human Motion Prediction
EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning
Joint-Relation Transformer for Multi-Person Motion Prediction
Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction
Fine-grained Temporal Contrastive Learning for Weakly-supervised Temporal Action Localization
Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity
Two-person Graph Convolutional Network for Skeleton-based Human Interaction Recognition
Simple online and real-time tracking with occlusion handling
DECODE: Domain-aware Continual Domain Expansion for Motion Prediction
When Did It Happen? Duration-informed Temporal Localization of Narrated Actions in Vlogs
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.