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 25762600 of 2759 papers

TitleStatusHype
Efficient Transfer Learning for Video-language Foundation ModelsCode0
Joint-Partition Group Attention for skeleton-based action recognitionCode0
Joint Mixing Data Augmentation for Skeleton-based Action RecognitionCode0
SpotFast Networks with Memory Augmented Lateral Transformers for LipreadingCode0
Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN FeaturesCode0
A Variational Graph Autoencoder for Manipulation Action Recognition and PredictionCode0
Using phase instead of optical flow for action recognitionCode0
Joint Discovery of Object States and Manipulation ActionsCode0
Real-World Graph Convolution Networks (RW-GCNs) for Action Recognition in Smart Video SurveillanceCode0
STAA: Spatio-Temporal Attention Attribution for Real-Time Interpreting Transformer-based Video ModelsCode0
Actional-Structural Graph Convolutional Networks for Skeleton-based Action RecognitionCode0
View Adaptive Neural Networks for High Performance Skeleton-based Human Action RecognitionCode0
Cross-Model Cross-Stream Learning for Self-Supervised Human Action RecognitionCode0
STAIR Actions: A Video Dataset of Everyday Home ActionsCode0
Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on VideosCode0
Efficient Human Vision Inspired Action Recognition using Adaptive Spatiotemporal SamplingCode0
AGAR: Attention Graph-RNN for Adaptative Motion Prediction of Point Clouds of Deformable ObjectsCode0
Deep Efficient Continuous Manifold Learning for Time Series ModelingCode0
Recognizing Involuntary Actions from 3D Skeleton Data Using Body StatesCode0
Recognizing Manipulation Actions from State-TransformationsCode0
Recognizing Video Events with Varying RhythmsCode0
Movie Genre Classification by Language Augmentation and Shot SamplingCode0
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton DataCode0
AENet: Learning Deep Audio Features for Video AnalysisCode0
Temporal Unet: Sample Level Human Action Recognition using WiFiCode0
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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