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 16761700 of 2759 papers

TitleStatusHype
Universal-to-Specific Framework for Complex Action Recognition0
IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in VideosCode1
Multitask Non-Autoregressive Model for Human Motion Prediction0
Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition0
Representation Learning via Adversarially-Contrastive Optimal Transport0
Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action TripletsCode1
AViD Dataset: Anonymized Videos from Diverse CountriesCode1
Learning Speech Representations from Raw Audio by Joint Audiovisual Self-Supervision0
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action RecognitionCode1
VPN: Learning Video-Pose Embedding for Activities of Daily LivingCode1
Complex Human Action Recognition in Live Videos Using Hybrid FR-DL Method0
Learning from Failure: Training Debiased Classifier from Biased ClassifierCode1
Quo Vadis, Skeleton Action Recognition ?Code1
Egocentric Action Recognition by Video Attention and Temporal Context0
Path Signatures on Lie GroupsCode0
Attention-Oriented Action Recognition for Real-Time Human-Robot Interaction0
Low-light Environment Neural SurveillanceCode0
JUMPS: Joints Upsampling Method for Pose Sequences0
Group Ensemble: Learning an Ensemble of ConvNets in a single ConvNetCode0
The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and PoseCode1
Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery0
Video Representation Learning with Visual Tempo ConsistencyCode1
Dynamic Sampling Networks for Efficient Action Recognition in Videos0
Roweisposes, Including Eigenposes, Supervised Eigenposes, and Fisherposes, for 3D Action RecognitionCode0
Unsupervised Learning of Video Representations via Dense Trajectory ClusteringCode1
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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