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 2251–2300 of 2759 papers

TitleStatusHype
Pose Encoding for Robust Skeleton-Based Action Recognition—0
Two Stream Self-Supervised Learning for Action Recognition—0
Massively Parallel Video Networks—0
Action4D: Real-time Action Recognition in the Crowd and Clutter—0
Videos as Space-Time Region Graphs—0
Squeeze-and-Excitation on Spatial and Temporal Deep Feature Space for Action Recognition—0
Learning and Using the Arrow of Time—0
Pulling Actions out of Context: Explicit Separation for Effective Combination—0
MiCT: Mixed 3D/2D Convolutional Tube for Human Action Recognition—0
Recognize Actions by Disentangling Components of Dynamics—0
Recognizing Human Actions as the Evolution of Pose Estimation Maps—0
PoTion: Pose MoTion Representation for Action Recognition—0
Making Convolutional Networks Recurrent for Visual Sequence Learning—0
PoseFlow: A Deep Motion Representation for Understanding Human Behaviors in Videos—0
Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning—0
RNN for Affects at SemEval-2018 Task 1: Formulating Affect Identification as a Binary Classification Problem—0
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition—0
SSNet: Scale Selection Network for Online 3D Action Prediction—0
Coding Kendall's Shape Trajectories for 3D Action Recognition—0
Temporal Hallucinating for Action Recognition With Few Still Images—0
A Fine-to-Coarse Convolutional Neural Network for 3D Human Action Recognition—0
Pose-Based Two-Stream Relational Networks for Action Recognition in Videos—0
DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization—0
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
Graph Edge Convolutional Neural Networks for Skeleton Based Action Recognition—0
Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning—0
Towards an Unequivocal Representation of Actions—0
Low-Latency Human Action Recognition with Weighted Multi-Region Convolutional Neural Network—0
Visual Attribute-augmented Three-dimensional Convolutional Neural Network for Enhanced Human Action Recognition—0
Relational Network for Skeleton-Based Action Recognition—0
Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning—0
Learning Optical Flow via Dilated Networks and Occlusion Reasoning—0
Object Activity Scene Description, Construction and Recognition—0
Unsupervised representation learning with long-term dynamics for skeleton based action recognition—0
Actor and Observer: Joint Modeling of First and Third-Person VideosCode0
ECO: Efficient Convolutional Network for Online Video UnderstandingCode0
Memory Attention Networks for Skeleton-based Action RecognitionCode0
View Adaptive Neural Networks for High Performance Skeleton-based Human Action RecognitionCode0
PM-GANs: Discriminative Representation Learning for Action Recognition Using Partial-modalities—0
STAIR Actions: A Video Dataset of Everyday Home ActionsCode0
Audio-Visual Scene Analysis with Self-Supervised Multisensory FeaturesCode0
End-to-End Learning of Motion Representation for Video UnderstandingCode0
DIY Human Action Data Set Generation—0
Non-Linear Temporal Subspace Representations for Activity Recognition—0
Video Representation Learning Using Discriminative Pooling—0
Towards Universal Representation for Unseen Action Recognition—0
T-RECS: Training for Rate-Invariant Embeddings by Controlling Speed for Action Recognition—0
Exploiting deep residual networks for human action recognition from skeletal data—0
Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks—0
Attention-based Temporal Weighted Convolutional Neural Network for Action Recognition—0
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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
4DejaVidTop-1 Accuracy77.2—Unverified
5InternVideoTop-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
3VideoMAE V2-g3-fold Accuracy99.6—Unverified
4OmniVec3-fold Accuracy99.6—Unverified
5BIKE3-fold Accuracy98.8—Unverified
6SMART3-fold Accuracy98.64—Unverified
7OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6—Unverified
8PERF-Net (multi-distilled S3D)3-fold Accuracy98.6—Unverified
9ZeroI2V ViT-L/143-fold Accuracy98.6—Unverified
10LGD-3D Two-stream3-fold Accuracy98.2—Unverified