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 951975 of 2759 papers

TitleStatusHype
Handcrafted Local Features are Convolutional Neural Networks0
FinePseudo: Improving Pseudo-Labelling through Temporal-Alignablity for Semi-Supervised Fine-Grained Action Recognition0
First Person Action Recognition Using Deep Learned Descriptors0
CLTA: Contents and Length-based Temporal Attention for Few-shot Action Recognition0
First-Take-All: Temporal Order-Preserving Hashing for 3D Action Videos0
Bag of Visual Words and Fusion Methods for Action Recognition: Comprehensive Study and Good Practice0
CMAE-V: Contrastive Masked Autoencoders for Video Action Recognition0
An Approach to Pose-Based Action Recognition0
Dynamic Hypergraph Convolutional Networks for Skeleton-Based Action Recognition0
Fitting, Comparison, and Alignment of Trajectories on Positive Semi-Definite Matrices with Application to Action Recognition0
Adversarial Domain Adaptation for Action Recognition Around the Clock0
Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition0
Flip-Invariant Motion Representation0
Knowledge Distillation for Human Action Anticipation0
FlowCaps: Optical Flow Estimation with Capsule Networks For Action Recognition0
Flow-Distilled IP Two-Stream Networks for Compressed Video Action Recognition0
Flow Dynamics Correction for Action Recognition0
Anchor-Based Spatio-Temporal Attention 3D Convolutional Networks for Dynamic 3D Point Cloud Sequences0
H2O: Two Hands Manipulating Objects for First Person Interaction Recognition0
Focal and Global Spatial-Temporal Transformer for Skeleton-based Action Recognition0
Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition0
Focusing and Diffusion: Bidirectional Attentive Graph Convolutional Networks for Skeleton-based Action Recognition0
Dynamic Appearance: A Video Representation for Action Recognition with Joint Training0
ForcePose: A Deep Learning Approach for Force Calculation Based on Action Recognition Using MediaPipe Pose Estimation Combined with Object Detection0
Dynamically Encoded Actions Based on Spacetime Saliency0
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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