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 22762300 of 2759 papers

TitleStatusHype
Depth Pooling Based Large-scale 3D Action Recognition with Convolutional Neural Networks0
Analysis of Hand Segmentation in the WildCode0
Sparse Adversarial Perturbations for VideosCode1
Learning clip representations for skeleton-based 3d action recognition0
Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition0
2D/3D Pose Estimation and Action Recognition using Multitask Deep LearningCode0
Real-Time End-to-End Action Detection with Two-Stream Networks0
Glimpse Clouds: Human Activity Recognition from Unstructured Feature PointsCode0
Learning Representative Temporal Features for Action Recognition0
Fisherposes for Human Action Recognition Using Kinect Sensor DataCode0
Musical Chair: Efficient Real-Time Recognition Using Collaborative IoT Devices0
Human Action Adverb Recognition: ADHA Dataset and A Three-Stream Hybrid Model0
Deep-Temporal LSTM for Daily Living Action Recognition0
Action Recognition with Spatio-Temporal Visual Attention on Skeleton Image Sequences0
Histogram of Oriented Depth Gradients for Action Recognition0
A Generative Approach to Zero-Shot and Few-Shot Action Recognition0
Let's Dance: Learning From Online Dance VideosCode0
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action RecognitionCode1
DiscrimNet: Semi-Supervised Action Recognition from Videos using Generative Adversarial Networks0
Multivariate LSTM-FCNs for Time Series ClassificationCode1
Semi-supervised Fisher vector network0
Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition0
Moments in Time Dataset: one million videos for event understandingCode0
Ensemble One-dimensional Convolution Neural Networks for Skeleton-based Action Recognition0
What have we learned from deep representations for action recognition?0
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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