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 1276–1300 of 1322 papers

TitleStatusHype
Understanding and Improving Deep Neural Network for Activity RecognitionCode0
Quantization and Deployment of Deep Neural Networks on MicrocontrollersCode0
Batch-based Activity Recognition from Egocentric Photo-Streams RevisitedCode0
Kernel Cross-CorrelatorCode0
Context-Aware Predictive Coding: A Representation Learning Framework for WiFi SensingCode0
Kernel learning for visual perceptionCode0
Semantically Encoding Activity Labels for Context-Aware Human Activity RecognitionCode0
RALACs: Action Recognition in Autonomous Vehicles using Interaction Encoding and Optical FlowCode0
Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge TransferCode0
Does SpatioTemporal information benefit Two video summarization benchmarks?Code0
AutoGCN -- Towards Generic Human Activity Recognition with Neural Architecture SearchCode0
The Visual Experience Dataset: Over 200 Recorded Hours of Integrated Eye Movement, Odometry, and Egocentric VideoCode0
An IoT Based Framework For Activity Recognition Using Deep Learning TechniqueCode0
KU-HAR: An open dataset for heterogeneous human activity recognitionCode0
A Hierarchical Deep Temporal Model for Group Activity RecognitionCode0
Label Leakage in Federated Inertial-based Human Activity RecognitionCode0
LaHAR: Latent Human Activity Recognition using LDACode0
DiTMoS: Delving into Diverse Tiny-Model Selection on MicrocontrollersCode0
Non-Uniform Subset Selection for Active Learning in Structured DataCode0
Action Recognition for Privacy-Preserving Ambient Assisted LivingCode0
Context-Aware Predictive Coding: A Representation Learning Framework for WiFi SensingCode0
WiFlexFormer: Efficient WiFi-Based Person-Centric SensingCode0
An Interactive Greedy Approach to Group Sparsity in High DimensionsCode0
Large-scale weakly-supervised pre-training for video action recognitionCode0
Large Transformers are Better EEG LearnersCode0
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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