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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1126–1150 of 1322 papers

TitleStatusHype
Understanding and Improving Deep Neural Network for Activity RecognitionCode0
Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling—0
Object and Text-guided Semantics for CNN-based Activity Recognition—0
Human Activity Recognition using Recurrent Neural Networks—0
M-PACT: An Open Source Platform for Repeatable Activity Classification ResearchCode0
Fine-grained Activity Recognition in Baseball VideosCode0
Question Type Guided Attention in Visual Question Answering—0
Non-Linear Temporal Subspace Representations for Activity Recognition—0
Modelling the Influence of Cultural Information on Vision-Based Human Home Activity Recognition—0
Dynamic Vision Sensors for Human Activity RecognitionCode0
Analysis of Hand Segmentation in the WildCode0
ReHAR: Robust and Efficient Human Activity Recognition—0
Glimpse Clouds: Human Activity Recognition from Unstructured Feature PointsCode0
Multimodal Explanations: Justifying Decisions and Pointing to the EvidenceCode0
Learning Attribute Representation for Human Activity Recognition—0
Personalized Human Activity Recognition Using Convolutional Neural Networks—0
When Vehicles See Pedestrians with Phones:A Multi-Cue Framework for Recognizing Phone-based Activities of Pedestrians—0
Human Activity Recognition for Mobile Robot—0
Semi-Supervised Convolutional Neural Networks for Human Activity Recognition—0
Time Series Segmentation through Automatic Feature Learning—0
Stratified Transfer Learning for Cross-domain Activity Recognition—0
Im2Flow: Motion Hallucination from Static Images for Action RecognitionCode0
Recognizing Plans by Learning Embeddings from Observed Action Distributions—0
Human activity recognition from mobile inertial sensors using recurrence plots—0
Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN FeaturesCode0
Show:102550
← PrevPage 46 of 53Next →
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