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 776–800 of 1322 papers

TitleStatusHype
Re-Sign: Re-Aligned End-To-End Sequence Modelling With Deep Recurrent CNN-HMMs—0
Resource-Efficient Computing in Wearable Systems—0
Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification—0
Resource-Eficient Continual Learning for Sensor-Based Human Activity Recognition—0
Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging—0
RISAR: RIS-assisted Human Activity Recognition with Commercial Wi-Fi Devices—0
Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learning—0
Robust Automated Human Activity Recognition and its Application to Sleep Research—0
Robust Multimodal Fusion for Human Activity Recognition—0
SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition—0
Robust Trajectory-based Density Estimation for Geometric Structure Recovery: Theory and Applications—0
RSA: Randomized Simulation as Augmentation for Robust Human Action Recognition—0
rTsfNet: a DNN model with Multi-head 3D Rotation and Time Series Feature Extraction for IMU-based Human Activity Recognition—0
rWISDM: Repaired WISDM, a Public Dataset for Human Activity Recognition—0
ScalableHD: Scalable and High-Throughput Hyperdimensional Computing Inference on Multi-Core CPUs—0
Scaling Human Activity Recognition: A Comparative Evaluation of Synthetic Data Generation and Augmentation Techniques—0
Scaling laws in wearable human activity recognition—0
Scaling Wearable Foundation Models—0
Scene Graph Generation with Geometric Context—0
Seeing What You're Told: Sentence-Guided Activity Recognition In Video—0
Seeker: Synergizing Mobile and Energy Harvesting Wearable Sensors for Human Activity Recognition—0
See No Evil, Say No Evil: Description Generation from Densely Labeled Images—0
Segmented convolutional gated recurrent neural networks for human activity recognition in ultra-wideband radar—0
Selective Feature Compression for Efficient Activity Recognition Inference—0
SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning—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