SOTAVerified

Activity Recognition

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

Papers

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Showing 651–700 of 1322 papers

TitleStatusHype
Multiscale Manifold Warping—0
Multi-Scale Supervised Network for Human Pose Estimation—0
Multi-Stage Based Feature Fusion of Multi-Modal Data for Human Activity Recognition—0
Multi-stage RGB-based Transfer Learning Pipeline for Hand Activity Recognition—0
Multi-task Self-Supervised Learning for Human Activity Detection—0
Multi-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass Procedures—0
Multi-Type Activity Recognition in Robot-Centric Scenarios—0
Multivariate Time Series Classification Using Dynamic Time Warping Template Selection for Human Activity Recognition—0
Ensemble Learning for Fusion of Multiview Vision with Occlusion and Missing Information: Framework and Evaluations with Real-World Data and Applications in Driver Hand Activity Recognition—0
Multi-View Fusion Transformer for Sensor-Based Human Activity Recognition—0
MU-MAE: Multimodal Masked Autoencoders-Based One-Shot Learning—0
MuMu: Cooperative Multitask Learning-based Guided Multimodal Fusion—0
MVSA-Net: Multi-View State-Action Recognition for Robust and Deployable Trajectory Generation—0
Natively neuromorphic LMU architecture for encoding-free SNN-based HAR on commercial edge devices—0
Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition—0
NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis—0
Nested Motion Descriptors—0
Neural Style Transfer Enhanced Training Support For Human Activity Recognition—0
Neuro-Symbolic Approaches for Context-Aware Human Activity Recognition—0
New Convex Relaxations for MRF Inference With Unknown Graphs—0
Non-Linear Temporal Subspace Representations for Activity Recognition—0
Non-local Graph Convolutional Network for joint Activity Recognition and Motion Prediction—0
Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity Recognition—0
Novel evaluation of surgical activity recognition models using task-based efficiency metrics—0
Nuisance-Label Supervision: Robustness Improvement by Free Labels—0
Nurse care activity recognition challenge: summary and results—0
Object and Text-guided Semantics for CNN-based Activity Recognition—0
Octave Mix: Data augmentation using frequency decomposition for activity recognition—0
On Attention Models for Human Activity Recognition—0
On Flow Profile Image for Video Representation—0
On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge—0
Online Collective Animal Movement Activity Recognition—0
To Store or Not? Online Data Selection for Federated Learning with Limited Storage—0
Online Feature Selection for Activity Recognition using Reinforcement Learning with Multiple Feedback—0
Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning—0
Online Human Activity Recognition Employing Hierarchical Hidden Markov Models—0
Online Human Activity Recognition using Low-Power Wearable Devices—0
On Matched Filtering for Statistical Change Point Detection—0
On Multi-resident Activity Recognition in Ambient Smart-Homes—0
On Neural Inertial Classification Networks for Pedestrian Activity Recognition—0
On the Benefit of Generative Foundation Models for Human Activity Recognition—0
On the recognition of the game type based on physiological signals and eye tracking—0
On the Role of Event Boundaries in Egocentric Activity Recognition from Photostreams—0
OpenPack: A Large-scale Dataset for Recognizing Packaging Works in IoT-enabled Logistic Environments—0
Optical Flow Estimation in 360^ Videos: Dataset, Model and Application—0
Optimised Convolutional Neural Networks for Heart Rate Estimation and Human Activity Recognition in Wrist Worn Sensing Applications—0
Optimized Gated Deep Learning Architectures for Sensor Fusion—0
Ordered Atomic Activity for Fine-grained Interactive Traffic Scenario Understanding—0
Order Matters: On Parameter-Efficient Image-to-Video Probing for Recognizing Nearly Symmetric Actions—0
Otago Exercises Monitoring for Older Adults by a Single IMU and Hierarchical Machine Learning Models—0
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All datasetsRWF-2000Stanford40First-Person Hand Action BenchmarkSelf-stimulatory Behavior Dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Structured Keypoint PoolingAccuracy93.4—Unverified
2Semi-Supervised Hard Attention (SSHA); pretrained on Deepmind Kinetics datasetAccuracy90.4—Unverified
3Human Skeletons + Change DetectionAccuracy90.25—Unverified
4Separable Convolutional LSTMAccuracy89.75—Unverified
5SPIL ConvolutionAccuracy89.3—Unverified
6Flow Gated NetworkAccuracy87.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FocusCLIPTop-3 Accuracy (%)10.47—Unverified
2CLIPTop-3 Accuracy (%)6.49—Unverified
#ModelMetricClaimedVerifiedStatus
1Boutaleb et al.1:1 Accuracy97.91—Unverified
#ModelMetricClaimedVerifiedStatus
1all-landmark-modelActivity Recognition0.76—Unverified