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 951–1000 of 1322 papers

TitleStatusHype
Personalized Activity Recognition with Deep Triplet EmbeddingsCode0
Towards Generalizable Surgical Activity Recognition Using Spatial Temporal Graph Convolutional Networks—0
Classification of human activity recognition using smartphones—0
Improve Unsupervised Domain Adaptation with Mixup Training—0
Feature engineering workflow for activity recognition from synchronized inertial measurement unitsCode0
Bonn Activity Maps: Dataset Description—0
Enabling Machine Learning Across Heterogeneous Sensor Networks with Graph Autoencoders—0
DASZL: Dynamic Action Signatures for Zero-shot Learning—0
Kernel learning for visual perceptionCode0
Hybrid Model Featuring CNN and LSTM Architecture for Human Activity Recognition on Smartphone Sensor Data—0
Template co-updating in multi-modal human activity recognition systems—0
RSA: Randomized Simulation as Augmentation for Robust Human Action Recognition—0
Skeleton based Activity Recognition by Fusing Part-wise Spatio-temporal and Attention Driven Residues—0
Towards Hardware-Aware Tractable Learning of Probabilistic ModelsCode0
Topological Machine Learning for Multivariate Time SeriesCode0
Towards Fairness in Visual Recognition: Effective Strategies for Bias MitigationCode0
A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features—0
Fusion of Deep Neural Networks for Activity Recognition: A Regular Vine Copula Based Approach—0
Simultaneous Implementation Features Extraction and Recognition Using C3D Network for WiFi-based Human Activity Recognition—0
RWF-2000: An Open Large Scale Video Database for Violence DetectionCode0
Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning—0
Chirality Nets for Human Pose RegressionCode0
Model enhancement and personalization using weakly supervised learning for multi-modal mobile sensing—0
A systematic review of smartphone-based human activity recognition for health research—0
Drive&Act: A Multi-Modal Dataset for Fine-Grained Driver Behavior Recognition in Autonomous Vehicles—0
Uncertainty-Aware Audiovisual Activity Recognition Using Deep Bayesian Variational Inference—0
New Convex Relaxations for MRF Inference With Unknown Graphs—0
Toyota Smarthome: Real-World Activities of Daily Living—0
Generating Fair Universal Representations using Adversarial Models—0
Sign Language Recognition Analysis using Multimodal Data—0
A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices—0
Simultaneous Segmentation and Recognition: Towards more accurate Ego Gesture Recognition—0
Meta-Learning for Few-Shot Time Series Classification—0
Activity recognition using ST-GCN with 3D motion data—0
Extreme Low Resolution Activity Recognition with Confident Spatial-Temporal Attention Transfer—0
Can a simple approach identify complex nurse care activity?—0
Nurse care activity recognition challenge: summary and results—0
Personalizing Smartwatch Based Activity Recognition Using Transfer Learning—0
Temporal Reasoning Graph for Activity Recognition—0
Multi-Modal Recognition of Worker Activity for Human-Centered Intelligent Manufacturing—0
Human activity recognition from skeleton posesCode0
Online Feature Selection for Activity Recognition using Reinforcement Learning with Multiple Feedback—0
Wi-Fringe: Leveraging Text Semantics in WiFi CSI-Based Device-Free Named Gesture Recognition—0
MEx: Multi-modal Exercises Dataset for Human Activity RecognitionCode0
Three Branches: Detecting Actions With Richer Features—0
Progressive Relation Learning for Group Activity Recognition—0
Discriminating Spatial and Temporal Relevance in Deep Taylor Decompositions for Explainable Activity RecognitionCode0
Mindful Active LearningCode0
Multi-task Self-Supervised Learning for Human Activity Detection—0
Multiple Human Association between Top and Horizontal Views by Matching Subjects' Spatial Distributions—0
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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