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

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Showing 1751–1800 of 2759 papers

TitleStatusHype
Towards Improved Human Action Recognition Using Convolutional Neural Networks and Multimodal Fusion of Depth and Inertial Sensor Data—0
Multidomain Multimodal Fusion For Human Action Recognition Using Inertial Sensors—0
Object Properties Inferring from and Transfer for Human Interaction Motions—0
Accuracy and Performance Comparison of Video Action Recognition Approaches—0
ConvGRU in Fine-grained Pitching Action Recognition for Action Outcome PredictionCode0
Look, Listen, and Attend: Co-Attention Network for Self-Supervised Audio-Visual Representation Learning—0
2nd Place Scheme on Action Recognition Track of ECCV 2020 VIPriors Challenges: An Efficient Optical Flow Stream Guided Framework—0
Richly Activated Graph Convolutional Network for Robust Skeleton-based Action RecognitionCode0
Exploring Relations in Untrimmed Videos for Self-Supervised Learning—0
Residual Frames with Efficient Pseudo-3D CNN for Human Action Recognition—0
Recognition and 3D Localization of Pedestrian Actions from Monocular Video—0
Vision and Inertial Sensing Fusion for Human Action Recognition : A Review—0
Learning Video Representations by Transforming Time—0
Shuffle and Attend: Video Domain Adaptation—0
Towards Efficient Coarse-to-Fine Networks for Action and Gesture Recognition—0
Learning Actionness via Long-range Temporal Order Verification—0
Improving Skeleton-based Action Recognitionwith Robust Spatial and Temporal Features—0
Self-supervised Motion Representation via Scattering Local Motion Cues—0
On Dropping Clusters to Regularize Graph Convolutional Neural Networks—0
CoTeRe-Net: Discovering Collaborative Ternary Relations in VideosCode0
A Recurrent Transformer Network for Novel View Action SynthesisCode0
Mix Dimension in Poincaré Geometry for 3D Skeleton-based Action Recognition—0
Hierarchical Action Classification with Network Pruning—0
Learning Video Representations from Textual Web Supervision—0
On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures—0
Representation Learning with Video Deep InfoMax—0
Deep-VFX: Deep Action Recognition Driven VFX for Short Video—0
Depthwise Spatio-Temporal STFT Convolutional Neural Networks for Human Action Recognition—0
Perceptron Synthesis Network: Rethinking the Action Scale Variances in Videos—0
Uncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos—0
Video Representation Learning by Recognizing Temporal Transformations—0
Directional Temporal Modeling for Action Recognition—0
Hierarchical Contrastive Motion Learning for Video Action Recognition—0
Challenge report:VIPriors Action Recognition Challenge—0
Learning End-to-End Action Interaction by Paired-Embedding Data Augmentation—0
Temporal Distinct Representation Learning for Action Recognition—0
Multitask Non-Autoregressive Model for Human Motion Prediction—0
Universal-to-Specific Framework for Complex Action Recognition—0
Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition—0
Representation Learning via Adversarially-Contrastive Optimal Transport—0
Learning Speech Representations from Raw Audio by Joint Audiovisual Self-Supervision—0
Complex Human Action Recognition in Live Videos Using Hybrid FR-DL Method—0
Egocentric Action Recognition by Video Attention and Temporal Context—0
JUMPS: Joints Upsampling Method for Pose Sequences—0
Path Signatures on Lie GroupsCode0
Low-light Environment Neural SurveillanceCode0
Attention-Oriented Action Recognition for Real-Time Human-Robot Interaction—0
Group Ensemble: Learning an Ensemble of ConvNets in a single ConvNetCode0
Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery—0
Roweisposes, Including Eigenposes, Supervised Eigenposes, and Fisherposes, for 3D Action RecognitionCode0
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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