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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 901–925 of 1322 papers

TitleStatusHype
Learning and Verification of Task Structure in Instructional Videos—0
Learning Attribute Representation for Human Activity Recognition—0
Learning Ensembles of Potential Functions for Structured Prediction With Latent Variables—0
Learning from Imbalanced Multiclass Sequential Data Streams Using Dynamically Weighted Conditional Random Fields—0
M3Act: Learning from Synthetic Human Group Activities—0
Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognition—0
Generating Fair Universal Representations using Adversarial Models—0
Learning Privately from Multiparty Data—0
Learning-to-Learn Personalised Human Activity Recognition Models—0
Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices—0
Leaving Some Stones Unturned: Dynamic Feature Prioritization for Activity Detection in Streaming Video—0
Lifelong Adaptive Machine Learning for Sensor-based Human Activity Recognition Using Prototypical Networks—0
LiGAR: LiDAR-Guided Hierarchical Transformer for Multi-Modal Group Activity Recognition—0
Lightweight Transformer in Federated Setting for Human Activity Recognition—0
Limitations in Employing Natural Language Supervision for Sensor-Based Human Activity Recognition -- And Ways to Overcome Them—0
Long Term Object Detection and Tracking in Collaborative Learning Environments—0
Low-power Spike-based Wearable Analytics on RRAM Crossbars—0
Low-Resolution Action Recognition for Tiny Actions Challenge—0
LSC-ADL: An Activity of Daily Living (ADL)-Annotated Lifelog Dataset Generated via Semi-Automatic Clustering—0
LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data—0
MAC-Gaze: Motion-Aware Continual Calibration for Mobile Gaze Tracking—0
Machine-Generated Hierarchical Structure of Human Activities to Reveal How Machines Think—0
Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review—0
MAGNETO: Edge AI for Human Activity Recognition -- Privacy and Personalization—0
Maintenance Required: Updating and Extending Bootstrapped Human Activity Recognition Systems for Smart Homes—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