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 9761000 of 2759 papers

TitleStatusHype
Videoprompter: an ensemble of foundational models for zero-shot video understanding0
S3Aug: Segmentation, Sampling, and Shift for Action Recognition0
Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey0
Deep Learning Techniques for Video Instance Segmentation: A Survey0
Flow Dynamics Correction for Action Recognition0
Few-shot Action Recognition with Captioning Foundation Models0
Proving the Potential of Skeleton Based Action Recognition to Automate the Analysis of Manual Processes0
SpikePoint: An Efficient Point-based Spiking Neural Network for Event Cameras Action Recognition0
Analyzing Zero-Shot Abilities of Vision-Language Models on Video Understanding Tasks0
Graph learning in robotics: a survey0
PoseAction: Action Recognition for Patients in the Ward using Deep Learning Approaches0
Beyond the Benchmark: Detecting Diverse Anomalies in VideosCode0
Action Recognition Utilizing YGAR Dataset0
A Hierarchical Graph-based Approach for Recognition and Description Generation of Bimanual Actions in Videos0
Telling Stories for Common Sense Zero-Shot Action RecognitionCode0
Position and Orientation-Aware One-Shot Learning for Medical Action Recognition from Signal Data0
M^33D: Learning 3D priors using Multi-Modal Masked Autoencoders for 2D image and video understanding0
Chop & Learn: Recognizing and Generating Object-State Compositions0
Boundary-Aware Proposal Generation Method for Temporal Action Localization0
Egocentric RGB+Depth Action Recognition in Industry-Like SettingsCode0
S3TC: Spiking Separated Spatial and Temporal Convolutions with Unsupervised STDP-based Learning for Action Recognition0
Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-SupervisionCode0
Survey of Action Recognition, Spotting and Spatio-Temporal Localization in Soccer -- Current Trends and Research Perspectives0
CPR-Coach: Recognizing Composite Error Actions based on Single-class Training0
SkeleTR: Towrads Skeleton-based Action Recognition in the Wild0
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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
4InternVideoTop-1 Accuracy77.2Unverified
5DejaVidTop-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
3OmniVec3-fold Accuracy99.6Unverified
4VideoMAE V2-g3-fold Accuracy99.6Unverified
5BIKE3-fold Accuracy98.8Unverified
6SMART3-fold Accuracy98.64Unverified
7ZeroI2V ViT-L/143-fold Accuracy98.6Unverified
8OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6Unverified
9PERF-Net (multi-distilled S3D)3-fold Accuracy98.6Unverified
10Text4Vis3-fold Accuracy98.2Unverified