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Activity Recognition
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Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.
Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
SuperEIO: Self-Supervised Event Feature Learning for Event Inertial Odometry
Action Recognition in Real-World Ambient Assisted Living Environment
Leaking LoRa: An Evaluation of Password Leaks and Knowledge Storage in Large Language Models
ForcePose: A Deep Learning Approach for Force Calculation Based on Action Recognition Using MediaPipe Pose Estimation Combined with Object Detection
DF-Net: The Digital Forensics Network for Image Forgery Detection
Data-driven worker activity recognition and picking efficiency estimation in manual strawberry harvesting
CMD-HAR: Cross-Modal Disentanglement for Wearable Human Activity Recognition
A Survey on (M)LLM-Based GUI Agents
Context-Aware Weakly Supervised Image Manipulation Localization with SAM Refinement
Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language Recognition
LLM Agents That Act Like Us: Accurate Human Behavior Simulation with Real-World Data
ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving Systems
fine-CLIP: Enhancing Zero-Shot Fine-Grained Surgical Action Recognition with Vision-Language Models
Surg-3M: A Dataset and Foundation Model for Perception in Surgical Settings
ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation
ATARS: An Aerial Traffic Atomic Activity Recognition and Temporal Segmentation Dataset
LLaVAction: evaluating and training multi-modal large language models for action recognition
PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition
Mind with Eyes: from Language Reasoning to Multimodal Reasoning
DGAR: A Unified Domain Generalization Framework for RF-Enabled Human Activity Recognition
Process Optimization and Deployment for Sensor-Based Human Activity Recognition Based on Deep Learning
Temporal-Guided Spiking Neural Networks for Event-Based Human Action Recognition
PRIMAL: Physically Reactive and Interactive Motor Model for Avatar Learning
CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners
DPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid Framework
Body-Hand Modality Expertized Networks with Cross-attention for Fine-grained Skeleton Action Recognition
LEGION: Learning to Ground and Explain for Synthetic Image Detection
Towards Achieving Perfect Multimodal Alignment
Multi-Modal Gesture Recognition from Video and Surgical Tool Pose Information via Motion Invariants
A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions
Towards Scalable Modeling of Compressed Videos for Efficient Action Recognition
HAR-DoReMi: Optimizing Data Mixture for Self-Supervised Human Activity Recognition Across Heterogeneous IMU Datasets
VA-AR: Learning Velocity-Aware Action Representations with Mixture of Window Attention
In Shift and In Variance: Assessing the Robustness of HAR Deep Learning Models against Variability
Diffuse-CLoC: Guided Diffusion for Physics-based Character Look-ahead Control
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