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 976–1000 of 1322 papers

TitleStatusHype
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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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