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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1851–1900 of 2759 papers

TitleStatusHype
Energy-based Periodicity Mining with Deep Features for Action Repetition Counting in Unconstrained Videos—0
Skeleton Based Action Recognition using a Stacked Denoising Autoencoder with Constraints of Privileged Information—0
Beyond the Camera: Neural Networks in World Coordinates—0
Unifying Graph Embedding Features with Graph Convolutional Networks for Skeleton-based Action Recognition—0
Self-Supervised Visual Learning by Variable Playback Speeds Prediction of a VideoCode0
The iCub multisensor datasets for robot and computer vision applications—0
Image-based OoD-Detector Principles on Graph-based Input Data in Human Action Recognition—0
Evolving Losses for Unsupervised Video Representation Learning—0
Human Action Recognition using Local Two-Stream Convolution Neural Network Features and Support Vector Machines—0
Knowledge Integration Networks for Action Recognition—0
A Survey on 3D Skeleton-Based Action Recognition Using Learning Method—0
Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition—0
Learning spatio-temporal representations with temporal squeeze pooling—0
Self-Supervised Joint Encoding of Motion and Appearance for First Person Action Recognition—0
Weakly-Supervised Multi-Person Action Recognition in 360^ Videos—0
Dynamic Inference: A New Approach Toward Efficient Video Action Recognition—0
GradMix: Multi-source Transfer across Domains and Tasks—0
FSD-10: A Dataset for Competitive Sports Content Analysis—0
Symbiotic Attention with Privileged Information for Egocentric Action Recognition—0
CTM: Collaborative Temporal Modeling for Action Recognition—0
Learning Class Regularized Features for Action Recognition—0
Poisson Kernel Avoiding Self-Smoothing in Graph Convolutional Networks—0
An Information-rich Sampling Technique over Spatio-Temporal CNN for Classification of Human Actions in Videos—0
3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos—0
Modality Compensation Network: Cross-Modal Adaptation for Action Recognition—0
Joint Visual-Temporal Embedding for Unsupervised Learning of Actions in Untrimmed Sequences—0
Human Action Performance using Deep Neuro-Fuzzy Recurrent Attention Model—0
Action Recognition and State Change Prediction in a Recipe Understanding Task Using a Lightweight Neural Network Model—0
Zero-Shot Activity Recognition with Videos—0
Context-Aware Cross-Attention for Skeleton-Based Human Action Recognition—0
MixTConv: Mixed Temporal Convolutional Kernels for Efficient Action Recogntion—0
Recognizing Video Events with Varying RhythmsCode0
Self-supervising Action Recognition by Statistical Moment and Subspace Descriptors—0
Few-shot Action Recognition with Permutation-invariant AttentionCode0
An Emerging Coding Paradigm VCM: A Scalable Coding Approach Beyond Feature and Signal—0
PGCN-TCA: Pseudo Graph Convolutional Network With Temporal and Channel-Wise Attention for Skeleton-Based Action Recognition—0
Human Action Recognition and Assessment via Deep Neural Network Self-Organization—0
Focusing and Diffusion: Bidirectional Attentive Graph Convolutional Networks for Skeleton-based Action Recognition—0
DMCL: Distillation Multiple Choice Learning for Multimodal Action RecognitionCode0
Adversarial Cross-Domain Action Recognition with Co-Attention—0
Something-Else: Compositional Action Recognition with Spatial-Temporal Interaction NetworksCode0
Vertex Feature Encoding and Hierarchical Temporal Modeling in a Spatial-Temporal Graph Convolutional Network for Action Recognition—0
Self-Attention Network for Skeleton-based Human Action Recognition—0
Mimetics: Towards Understanding Human Actions Out of Context—0
Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional NetworksCode0
Multi-task Deep Learning for Real-Time 3D Human Pose Estimation and Action RecognitionCode0
SPIN: A High Speed, High Resolution Vision Dataset for Tracking and Action Recognition in Ping Pong—0
Totally Deep Support Vector Machines—0
Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action RecognitionCode0
Hidden Markov Model: Tutorial—0
Show:102550
← PrevPage 38 of 56Next →
All datasetsSomething-Something V2UCF101HMDB-51Something-Something V1AVA v2.2EPIC-KITCHENS-100NTU RGB+DNTU RGB+D 120Diving-48ActivityNetAVA v2.1H2O (2 Hands and Objects)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MViTv2-B (IN-21K + Kinetics400 pretrain)Top-5 Accuracy93.4—Unverified
2RSANet-R50 (8+16 frames, ImageNet pretrained, 2 clips)Top-5 Accuracy91.1—Unverified
3MVD (Kinetics400 pretrain, ViT-H, 16 frame)Top-1 Accuracy77.3—Unverified
4InternVideoTop-1 Accuracy77.2—Unverified
5DejaVidTop-1 Accuracy77.2—Unverified
6InternVideo2-1BTop-1 Accuracy77.1—Unverified
7VideoMAE V2-gTop-1 Accuracy77—Unverified
8MVD (Kinetics400 pretrain, ViT-L, 16 frame)Top-1 Accuracy76.7—Unverified
9Hiera-L (no extra data)Top-1 Accuracy76.5—Unverified
10TubeViT-LTop-1 Accuracy76.1—Unverified
#ModelMetricClaimedVerifiedStatus
1FTP-UniFormerV2-L/143-fold Accuracy99.7—Unverified
2OmniVec23-fold Accuracy99.6—Unverified
3OmniVec3-fold Accuracy99.6—Unverified
4VideoMAE V2-g3-fold Accuracy99.6—Unverified
5BIKE3-fold Accuracy98.8—Unverified
6SMART3-fold Accuracy98.64—Unverified
7ZeroI2V ViT-L/143-fold Accuracy98.6—Unverified
8OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6—Unverified
9PERF-Net (multi-distilled S3D)3-fold Accuracy98.6—Unverified
10Text4Vis3-fold Accuracy98.2—Unverified