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 301–325 of 1322 papers

TitleStatusHype
Decoupled Prompt-Adapter Tuning for Continual Activity Recognition—0
CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering—0
Evaluation of Encoding Schemes on Ubiquitous Sensor Signal for Spiking Neural Network—0
Guidelines for Augmentation Selection in Contrastive Learning for Time Series ClassificationCode0
Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space—0
Sensor-Aware Classifiers for Energy-Efficient Time Series Applications on IoT Devices—0
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community—0
GeoWATCH for Detecting Heavy Construction in Heterogeneous Time Series of Satellite Images—0
Self-supervised Learning via Cluster Distance Prediction for Operating Room Context Awareness—0
Topological Persistence Guided Knowledge Distillation for Wearable Sensor Data—0
Natively neuromorphic LMU architecture for encoding-free SNN-based HAR on commercial edge devices—0
VCHAR:Variance-Driven Complex Human Activity Recognition framework with Generative Representation—0
Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge TransferCode0
Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing DataCode0
Towards LLM-Powered Ambient Sensor Based Multi-Person Human Activity Recognition—0
Feature Fusion for Human Activity Recognition using Parameter-Optimized Multi-Stage Graph Convolutional Network and Transformer Models—0
Leveraging LDA Feature Extraction to Augment Human Activity Recognition AccuracyCode0
Self-supervised Multi-actor Social Activity Understanding in Streaming Videos—0
Maintenance Required: Updating and Extending Bootstrapped Human Activity Recognition Systems for Smart Homes—0
Game of LLMs: Discovering Structural Constructs in Activities using Large Language Models—0
Unsupervised explainable activity prediction in competitive Nordic Walking from experimental data—0
EarDA: Towards Accurate and Data-Efficient Earable Activity Sensing—0
Initial Investigation of Kolmogorov-Arnold Networks (KANs) as Feature Extractors for IMU Based Human Activity Recognition—0
GPT-4o: Visual perception performance of multimodal large language models in piglet activity understanding—0
Enhancing Activity Recognition After Stroke: Generative Adversarial Networks for Kinematic Data Augmentation—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