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 51–100 of 1322 papers

TitleStatusHype
MOMA-LRG: Language-Refined Graphs for Multi-Object Multi-Actor Activity ParsingCode1
SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity RecognitionCode1
Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box AttackCode1
Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural NetworksCode1
IMU2CLIP: Multimodal Contrastive Learning for IMU Motion Sensors from Egocentric Videos and TextCode1
Temporal Feature Alignment in Contrastive Self-Supervised Learning for Human Activity RecognitionCode1
Lightweight Transformers for Human Activity Recognition on Mobile DevicesCode1
SFusion: Self-attention based N-to-One Multimodal Fusion BlockCode1
Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and ApplicationsCode1
Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable DataCode1
Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity RecognitionCode1
CholecTriplet2021: A benchmark challenge for surgical action triplet recognitionCode1
Multimodal Transformer for Nursing Activity RecognitionCode1
SPAct: Self-supervised Privacy Preservation for Action RecognitionCode1
Knowledge Mining with Scene Text for Fine-Grained RecognitionCode1
Audio-Adaptive Activity Recognition Across Video DomainsCode1
Bridge-Prompt: Towards Ordinal Action Understanding in Instructional VideosCode1
SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge IntelligenceCode1
Panoramic Human Activity RecognitionCode1
HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor DataCode1
TransDARC: Transformer-based Driver Activity Recognition with Latent Space Feature CalibrationCode1
Wearable Sensor-Based Human Activity Recognition with Transformer ModelCode1
Learning Disentangled Behaviour Patterns for Wearable-based Human Activity RecognitionCode1
What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?Code1
COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only ModalityCode1
PartImageNet: A Large, High-Quality Dataset of PartsCode1
CubeLearn: End-to-end Learning for Human Motion Recognition from Raw mmWave Radar SignalsCode1
RF-Net: a Unified Meta-learning Framework for RF-enabled One-shot Human Activity RecognitionCode1
A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and ComparisonCode1
OPERAnet: A Multimodal Activity Recognition Dataset Acquired from Radio Frequency and Vision-based SensorsCode1
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial LearningCode1
Transformer Networks for Data Augmentation of Human Physical Activity RecognitionCode1
GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal TransformerCode1
Spatio-Temporal Dynamic Inference Network for Group Activity RecognitionCode1
Classification of Abnormal Hand Movement for Aiding in Autism Detection: Machine Learning StudyCode1
Transfer Learning for Pose Estimation of Illustrated CharactersCode1
Improving Deep Learning for HAR with shallow LSTMsCode1
Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video GamesCode1
Meta-HAR: Federated Representation Learning for Human Activity RecognitionCode1
Learning Group Activities from Skeletons without Individual Action LabelsCode1
Ego-Exo: Transferring Visual Representations from Third-person to First-person VideosCode1
SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access PointsCode1
Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using SmartphonesCode1
BASAR:Black-box Attack on Skeletal Action RecognitionCode1
Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity RecognitionCode1
Efficient Two-Stream Network for Violence Detection Using Separable Convolutional LSTMCode1
SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled DataCode1
Human Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation BenchmarkCode1
Interactive Fusion of Multi-level Features for Compositional Activity RecognitionCode1
Exploring Contrastive Learning in Human Activity Recognition for HealthcareCode1
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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