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 22012225 of 2759 papers

TitleStatusHype
DWnet: Deep-Wide Network for 3D Action Recognition0
Dynamic Action Recognition: A convolutional neural network model for temporally organized joint location data0
Dynamically Encoded Actions Based on Spacetime Saliency0
Dynamic Appearance: A Video Representation for Action Recognition with Joint Training0
Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition0
Dynamic Hypergraph Convolutional Networks for Skeleton-Based Action Recognition0
Dynamic Inference: A New Approach Toward Efficient Video Action Recognition0
Dynamic Matrix Decomposition for Action Recognition0
Dynamic Probabilistic Network Based Human Action Recognition0
Dynamic Sampling Networks for Efficient Action Recognition in Videos0
Dynamic Spatial-temporal Hypergraph Convolutional Network for Skeleton-based Action Recognition0
Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action Recognition0
DynamoNet: Dynamic Action and Motion Network0
EAGLE: Egocentric AGgregated Language-video Engine0
Early Action Recognition with Action Prototypes0
EA-VTR: Event-Aware Video-Text Retrieval0
EdgeOAR: Real-time Online Action Recognition On Edge Devices0
Effective Action Recognition with Embedded Key Point Shifts0
Efficient Action Detection in Untrimmed Videos via Multi-Task Learning0
Efficient Action Localization with Approximately Normalized Fisher Vectors0
Efficient Action Recognition Using Confidence Distillation0
Efficient Action Recognition via Dynamic Knowledge Propagation0
Efficient Attention-free Video Shift Transformers0
Efficient Feature Extraction, Encoding and Classification for Action Recognition0
Efficient Modelling Across Time of Human Actions and Interactions0
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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