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 326–350 of 1322 papers

TitleStatusHype
Video-based Exercise Classification and Activated Muscle Group Prediction with Hybrid X3D-SlowFast Network—0
Large Language Models Memorize Sensor Datasets! Implications on Human Activity Recognition Research—0
Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition—0
MuJo: Multimodal Joint Feature Space Learning for Human Activity Recognition—0
FLOW: Fusing and Shuffling Global and Local Views for Cross-User Human Activity Recognition with IMUs—0
iKAN: Global Incremental Learning with KAN for Human Activity Recognition Across Heterogeneous Datasets—0
HENASY: Learning to Assemble Scene-Entities for Egocentric Video-Language Model—0
Estimating Human Poses Across Datasets: A Unified Skeleton and Multi-Teacher Distillation Approach—0
Flow-Assisted Motion Learning Network for Weakly-Supervised Group Activity Recognition—0
Wearable-based behaviour interpolation for semi-supervised human activity recognition—0
NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis—0
Beyond Isolated Frames: Enhancing Sensor-Based Human Activity Recognition through Intra- and Inter-Frame Attention—0
A Masked Semi-Supervised Learning Approach for Otago Micro Labels Recognition—0
A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation—0
Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)—0
AdaFPP: Adapt-Focused Bi-Propagating Prototype Learning for Panoramic Activity Recognition—0
Millimeter Wave Radar-based Human Activity Recognition for Healthcare Monitoring Robot—0
SoK: Behind the Accuracy of Complex Human Activity Recognition Using Deep Learning—0
Unimodal and Multimodal Sensor Fusion for Wearable Activity Recognition—0
Meta-Decomposition: Dynamic Segmentation Approach Selection in IoT-based Activity Recognition—0
Design and Analysis of Efficient Attention in Transformers for Social Group Activity Recognition—0
A Survey on Multimodal Wearable Sensor-based Human Action Recognition—0
A Transformer-Based Model for the Prediction of Human Gaze Behavior on Videos—0
Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition—0
ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised Action Recognition in VideosCode0
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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