SOTAVerified

Action Recognition

Action Recognition is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.

In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos.

Please note some benchmarks may be located in the Action Classification or Video Classification tasks, e.g. Kinetics-400.

Papers

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Showing 1601–1650 of 2759 papers

TitleStatusHype
Ensemble One-dimensional Convolution Neural Networks for Skeleton-based Action Recognition—0
Ensembles of Deep Neural Networks for Action Recognition in Still Images—0
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2021: Team M3EM Technical Report—0
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2022: Team HNU-FPV Technical Report—0
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge: Mixed Sequences Prediction—0
Evaluating Transformers for Lightweight Action Recognition—0
Evaluation of Color STIPs for Human Action Recognition—0
Event and Activity Recognition in Video Surveillance for Cyber-Physical Systems—0
Event-based Action Recognition Using Timestamp Image Encoding Network—0
Event-based Timestamp Image Encoding Network for Human Action Recognition and Anticipation—0
EventCrab: Harnessing Frame and Point Synergy for Event-based Action Recognition and Beyond—0
Event Masked Autoencoder: Point-wise Action Recognition with Event-Based Cameras—0
EventTransAct: A video transformer-based framework for Event-camera based action recognition—0
Event Transformer+. A multi-purpose solution for efficient event data processing—0
Evolution-Preserving Dense Trajectory Descriptors—0
Evolving Losses for Unsupervised Video Representation Learning—0
Evolving Skeletons: Motion Dynamics in Action Recognition—0
Evolving Space-Time Neural Architectures for Videos—0
Examining Interpretable Feature Relationships in Deep Networks for Action recognition—0
EXMOVES: Classifier-based Features for Scalable Action Recognition—0
Egocentric and Exocentric Methods: A Short Survey—0
Expanded Parts Model for Human Attribute and Action Recognition in Still Images—0
Expansion-Squeeze-Excitation Fusion Network for Elderly Activity Recognition—0
Exploiting deep residual networks for human action recognition from skeletal data—0
Exploiting Inter-Frame Regional Correlation for Efficient Action Recognition—0
Exploiting Motion Information from Unlabeled Videos for Static Image Action Recognition—0
Exploiting Spatial-Temporal Modelling and Multi-Modal Fusion for Human Action Recognition—0
Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks—0
Recent Advances of Generic Object Detection with Deep Learning: A Review—0
Recent Progress in Appearance-based Action Recognition—0
Recognition and 3D Localization of Pedestrian Actions from Monocular Video—0
Recognize Actions by Disentangling Components of Dynamics—0
Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised Data—0
Recognizing Actions in Videos from Unseen Viewpoints—0
Recognizing Activities by Attribute Dynamics—0
Recognizing Human Actions as the Evolution of Pose Estimation Maps—0
Reconfigurable Cyber-Physical System for Lifestyle Video-Monitoring via Deep Learning—0
Reconstructing Humpty Dumpty: Multi-feature Graph Autoencoder for Open Set Action Recognition—0
Reconstruction-free action inference from compressive imagers—0
Recovering Complete Actions for Cross-dataset Skeleton Action Recognition—0
Recovering Trajectories of Unmarked Joints in 3D Human Actions Using Latent Space Optimization—0
Recurrent Residual Learning for Action Recognition—0
Recurring the Transformer for Video Action Recognition—0
Recursive Frechet Mean Computation on the Grassmannian and its Applications to Computer Vision—0
Reformulating Zero-shot Action Recognition for Multi-label Actions—0
ReGen: A good Generative Zero-Shot Video Classifier Should be Rewarded—0
REGINA - Reasoning Graph Convolutional Networks in Human Action Recognition—0
Region-aware Image-based Human Action Retrieval with Transformers—0
Regularization on Spatio-Temporally Smoothed Feature for Action Recognition—0
Regularizing Long Short Term Memory With 3D Human-Skeleton Sequences for Action Recognition—0
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All datasetsSomething-Something V2UCF101HMDB-51Something-Something V1AVA v2.2EPIC-KITCHENS-100NTU RGB+DNTU RGB+D 120Diving-48ActivityNetAVA v2.1H2O (2 Hands and Objects)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MViTv2-B (IN-21K + Kinetics400 pretrain)Top-5 Accuracy93.4—Unverified
2RSANet-R50 (8+16 frames, ImageNet pretrained, 2 clips)Top-5 Accuracy91.1—Unverified
3MVD (Kinetics400 pretrain, ViT-H, 16 frame)Top-1 Accuracy77.3—Unverified
4DejaVidTop-1 Accuracy77.2—Unverified
5InternVideoTop-1 Accuracy77.2—Unverified
6InternVideo2-1BTop-1 Accuracy77.1—Unverified
7VideoMAE V2-gTop-1 Accuracy77—Unverified
8MVD (Kinetics400 pretrain, ViT-L, 16 frame)Top-1 Accuracy76.7—Unverified
9Hiera-L (no extra data)Top-1 Accuracy76.5—Unverified
10TubeViT-LTop-1 Accuracy76.1—Unverified
#ModelMetricClaimedVerifiedStatus
1FTP-UniFormerV2-L/143-fold Accuracy99.7—Unverified
2OmniVec23-fold Accuracy99.6—Unverified
3VideoMAE V2-g3-fold Accuracy99.6—Unverified
4OmniVec3-fold Accuracy99.6—Unverified
5BIKE3-fold Accuracy98.8—Unverified
6SMART3-fold Accuracy98.64—Unverified
7OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6—Unverified
8PERF-Net (multi-distilled S3D)3-fold Accuracy98.6—Unverified
9ZeroI2V ViT-L/143-fold Accuracy98.6—Unverified
10LGD-3D Two-stream3-fold Accuracy98.2—Unverified