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 1176–1200 of 1322 papers

TitleStatusHype
Towards Fairness in Visual Recognition: Effective Strategies for Bias MitigationCode0
Mindful Active LearningCode0
Unsupervised Deep Learning-based clustering for Human Activity RecognitionCode0
Robotic Vision and Multi-View Synergy: Action and activity recognition in assisted living scenariosCode0
Towards Hardware-Aware Tractable Learning of Probabilistic ModelsCode0
Human Activity Recognition: A Spatio-temporal Image Encoding of 3D Skeleton Data for Online Action DetectionCode0
MMTSA: Multimodal Temporal Segment Attention Network for Efficient Human Activity RecognitionCode0
Personalized Activity Recognition with Deep Triplet EmbeddingsCode0
Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-ArtCode0
Deep Learning for Sensor-based Activity Recognition: A SurveyCode0
Adversarial Domain Adaptation for Cross-user Activity Recognition Using Diffusion-based Noise-centred LearningCode0
MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPUCode0
Human activity recognition from skeleton posesCode0
Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptationCode0
Accoustate: Auto-annotation of IMU-generated Activity Signatures under Smart InfrastructureCode0
Human Activity Recognition in an Open WorldCode0
Zero-Shot Activity Recognition with Verb Attribute InductionCode0
Adversarial Attacks on Deep Neural Networks for Time Series ClassificationCode0
Towards Multi-User Activity Recognition through Facilitated Training Data and Deep Learning for Human-Robot Collaboration ApplicationsCode0
Situation Recognition: Visual Semantic Role Labeling for Image UnderstandingCode0
Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity RecognitionCode0
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical BayesCode0
RWF-2000: An Open Large Scale Video Database for Violence DetectionCode0
Skeleton-Based Action Recognition with Spatial-Structural Graph ConvolutionCode0
SALIENCE: An Unsupervised User Adaptation Model for Multiple Wearable Sensors Based Human Activity RecognitionCode0
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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