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 19762000 of 2759 papers

TitleStatusHype
TARN: Temporal Attentive Relation Network for Few-Shot and Zero-Shot Action Recognition0
Real-Time Driver State Monitoring Using a CNN Based Spatio-Temporal Approach0
I Know the Relationships: Zero-Shot Action Recognition via Two-Stream Graph Convolutional Networks and Knowledge GraphsCode0
Spatiotemporal graph routing for skeleton-based action recognition0
A Unified Deep Framework for Joint 3D Pose Estimation and Action Recognition from a Single RGB Camera0
Slow Feature Analysis for Human Action Recognition0
Two-stream Spatiotemporal Feature for Video QA Task0
Video Action Recognition Via Neural Architecture Searching0
Learning Shape-Motion Representations from Geometric Algebra Spatio-Temporal Model for Skeleton-Based Action Recognition0
A Deep Learning Approach for Real-Time 3D Human Action Recognition from Skeletal Data0
Non-Local Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
Deformable Tube Network for Action Detection in Videos0
An Analysis of Deep Neural Networks with Attention for Action Recognition from a Neurophysiological Perspective0
Few-Shot Video Classification via Temporal Alignment0
A Comparative Review of Recent Kinect-based Action Recognition AlgorithmsCode0
Baidu-UTS Submission to the EPIC-Kitchens Action Recognition Challenge 20190
FBK-HUPBA Submission to the EPIC-Kitchens 2019 Action Recognition Challenge0
R-STAN: Residual Spatial-Temporal Attention Network for Action Recognition0
An Action Recognition network for specific target based on rMC and RPN0
Unsupervised Learning of Object Structure and Dynamics from Videos0
Spatio-Temporal Fusion Networks for Action Recognition0
Towards Real-Time Action Recognition on Mobile Devices Using Deep Models0
A Temporal Sequence Learning for Action Recognition and Prediction0
Three-Stream Convolutional Neural Network With Multi-Task and Ensemble Learning for 3D Action Recognition0
Accelerating temporal action proposal generation via high performance computing0
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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