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 76–100 of 1322 papers

TitleStatusHype
Deep Unsupervised Domain Adaptation for Time Series Classification: a BenchmarkCode1
Action-slot: Visual Action-centric Representations for Multi-label Atomic Activity Recognition in Traffic ScenesCode1
Efficient Two-Stream Network for Violence Detection Using Separable Convolutional LSTMCode1
Exploring Few-Shot Adaptation for Activity Recognition on Diverse DomainsCode1
A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and ComparisonCode1
Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and ApplicationsCode1
Generating Virtual On-body Accelerometer Data from Virtual Textual Descriptions for Human Activity RecognitionCode1
Audio-Adaptive Activity Recognition Across Video DomainsCode1
Bridge-Prompt: Towards Ordinal Action Understanding in Instructional VideosCode1
HHAR-net: Hierarchical Human Activity Recognition using Neural NetworksCode1
Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity RecognitionCode1
Human Activity Segmentation Challenge @ ECML/PKDD’23Code1
Human skeletons and change detection for efficient violence detection in surveillance videosCode1
IMU2CLIP: Multimodal Contrastive Learning for IMU Motion Sensors from Egocentric Videos and TextCode1
IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity RecognitionCode1
Knowledge Mining with Scene Text for Fine-Grained RecognitionCode1
LaMPP: Language Models as Probabilistic Priors for Perception and ActionCode1
Learning Generalizable Physiological Representations from Large-scale Wearable DataCode1
Learning Group Activities from Skeletons without Individual Action LabelsCode1
Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity RecognitionCode1
MOMA-LRG: Language-Refined Graphs for Multi-Object Multi-Actor Activity ParsingCode1
Multimodal Transformer for Nursing Activity RecognitionCode1
Multi-stage Learning for Radar Pulse Activity SegmentationCode1
Exploring Contrastive Learning in Human Activity Recognition for HealthcareCode1
Mobile Sensor Data AnonymizationCode1
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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