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
Unfolding Videos Dynamics via Taylor Expansion0
Unified Contrastive Fusion Transformer for Multimodal Human Action Recognition0
Unified Keypoint-based Action Recognition Framework via Structured Keypoint Pooling0
Unified Pose Sequence Modeling0
Unifying Few- and Zero-Shot Egocentric Action Recognition0
UniHPE: Towards Unified Human Pose Estimation via Contrastive Learning0
Universal Prototype Transport for Zero-Shot Action Recognition and Localization0
Universal-to-Specific Framework for Complex Action Recognition0
Unlimited Knowledge Distillation for Action Recognition in the Dark0
Unmanned Aerial Vehicle Control Through Domain-based Automatic Speech Recognition0
Unrepresentative video data: A review and evaluation0
Unseen Action Recognition with Unpaired Adversarial Multimodal Learning0
Unsupervised Action Proposal Ranking through Proposal Recombination0
Unsupervised Discriminative Embedding for Sub-Action Learning in Complex Activities0
Unsupervised Domain Adaptation for Action Recognition via Self-Ensembling and Conditional Embedding Alignment0
Unsupervised Domain Adaptation for Zero-Shot Learning0
Unsupervised Few-Shot Action Recognition via Action-Appearance Aligned Meta-Adaptation0
Unsupervised Learning for Human Sensing Using Radio Signals0
Unsupervised Learning of Object Structure and Dynamics from Videos0
Unsupervised Motion Representation Enhanced Network for Action Recognition0
Unsupervised representation learning with long-term dynamics for skeleton based action recognition0
Unsupervised Spatial-Temporal Feature Enrichment and Fidelity Preservation Network for Skeleton based Action Recognition0
Unsupervised Spectral Dual Assignment Clustering of Human Actions in Context0
Unsupervised Video Representation Learning by Bidirectional Feature Prediction0
Unsupervised View-Invariant Human Posture Representation0
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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