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

Showing 16011625 of 2759 papers

TitleStatusHype
Ensemble One-dimensional Convolution Neural Networks for Skeleton-based Action Recognition0
Ensembles of Deep Neural Networks for Action Recognition in Still Images0
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2021: Team M3EM Technical Report0
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2022: Team HNU-FPV Technical Report0
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge: Mixed Sequences Prediction0
Evaluating Transformers for Lightweight Action Recognition0
Evaluation of Color STIPs for Human Action Recognition0
Event and Activity Recognition in Video Surveillance for Cyber-Physical Systems0
Event-based Action Recognition Using Timestamp Image Encoding Network0
Event-based Timestamp Image Encoding Network for Human Action Recognition and Anticipation0
EventCrab: Harnessing Frame and Point Synergy for Event-based Action Recognition and Beyond0
Event Masked Autoencoder: Point-wise Action Recognition with Event-Based Cameras0
EventTransAct: A video transformer-based framework for Event-camera based action recognition0
Event Transformer+. A multi-purpose solution for efficient event data processing0
Evolution-Preserving Dense Trajectory Descriptors0
Evolving Losses for Unsupervised Video Representation Learning0
Evolving Skeletons: Motion Dynamics in Action Recognition0
Evolving Space-Time Neural Architectures for Videos0
Examining Interpretable Feature Relationships in Deep Networks for Action recognition0
EXMOVES: Classifier-based Features for Scalable Action Recognition0
Egocentric and Exocentric Methods: A Short Survey0
Expanded Parts Model for Human Attribute and Action Recognition in Still Images0
Expansion-Squeeze-Excitation Fusion Network for Elderly Activity Recognition0
Exploiting deep residual networks for human action recognition from skeletal data0
Exploiting Inter-Frame Regional Correlation for Efficient Action Recognition0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MViTv2-B (IN-21K + Kinetics400 pretrain)Top-5 Accuracy93.4Unverified
2RSANet-R50 (8+16 frames, ImageNet pretrained, 2 clips)Top-5 Accuracy91.1Unverified
3MVD (Kinetics400 pretrain, ViT-H, 16 frame)Top-1 Accuracy77.3Unverified
4DejaVidTop-1 Accuracy77.2Unverified
5InternVideoTop-1 Accuracy77.2Unverified
6InternVideo2-1BTop-1 Accuracy77.1Unverified
7VideoMAE V2-gTop-1 Accuracy77Unverified
8MVD (Kinetics400 pretrain, ViT-L, 16 frame)Top-1 Accuracy76.7Unverified
9Hiera-L (no extra data)Top-1 Accuracy76.5Unverified
10TubeViT-LTop-1 Accuracy76.1Unverified
#ModelMetricClaimedVerifiedStatus
1FTP-UniFormerV2-L/143-fold Accuracy99.7Unverified
2OmniVec23-fold Accuracy99.6Unverified
3VideoMAE V2-g3-fold Accuracy99.6Unverified
4OmniVec3-fold Accuracy99.6Unverified
5BIKE3-fold Accuracy98.8Unverified
6SMART3-fold Accuracy98.64Unverified
7OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6Unverified
8PERF-Net (multi-distilled S3D)3-fold Accuracy98.6Unverified
9ZeroI2V ViT-L/143-fold Accuracy98.6Unverified
10LGD-3D Two-stream3-fold Accuracy98.2Unverified