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 101–125 of 1322 papers

TitleStatusHype
Classification of Abnormal Hand Movement for Aiding in Autism Detection: Machine Learning StudyCode1
Bridge-Prompt: Towards Ordinal Action Understanding in Instructional VideosCode1
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised LearningCode1
Deep Unsupervised Domain Adaptation for Time Series Classification: a BenchmarkCode1
SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled DataCode1
GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal TransformerCode1
Dual-path Adaptation from Image to Video TransformersCode1
Self-supervised transfer learning of physiological representations from free-living wearable dataCode1
Semi-Supervised Online Structure Learning for Composite Event RecognitionCode1
Learning Generalizable Physiological Representations from Large-scale Wearable DataCode1
OSL𝛼: Online Structure Learning Using Background Knowledge AxiomatizationCode1
Spatio-Temporal Dynamic Inference Network for Group Activity RecognitionCode1
Efficient Two-Stream Network for Violence Detection Using Separable Convolutional LSTMCode1
Ego-Exo: Transferring Visual Representations from Third-person to First-person VideosCode1
ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor dataCode1
Temporal Feature Alignment in Contrastive Self-Supervised Learning for Human Activity RecognitionCode1
SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access PointsCode1
Time Series Segmentation Applied to a New Data Set for Mobile Sensing of Human ActivitiesCode1
Exploring Contrastive Learning in Human Activity Recognition for HealthcareCode1
Transformer Networks for Data Augmentation of Human Physical Activity RecognitionCode1
Exploring Few-Shot Adaptation for Activity Recognition on Diverse DomainsCode1
Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box AttackCode1
Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive LearningCode1
Fine-grained Activity Recognition in Baseball VideosCode0
An IoT Based Framework For Activity Recognition Using Deep Learning TechniqueCode0
Show:102550
← PrevPage 5 of 53Next →
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