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 551575 of 2759 papers

TitleStatusHype
Compressing Recurrent Neural Networks with Tensor Ring for Action RecognitionCode1
Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANsCode1
ARBEE: Towards Automated Recognition of Bodily Expression of Emotion In the WildCode1
Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot StudyCode1
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical AggregationCode1
SoccerNet: A Scalable Dataset for Action Spotting in Soccer VideosCode1
Sparse Adversarial Perturbations for VideosCode1
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action RecognitionCode1
Multivariate LSTM-FCNs for Time Series ClassificationCode1
A Closer Look at Spatiotemporal Convolutions for Action RecognitionCode1
Learning Spatio-Temporal Representation with Pseudo-3D Residual NetworksCode1
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video ClassificationCode1
Non-local Neural NetworksCode1
Grad-CAM++: Improved Visual Explanations for Deep Convolutional NetworksCode1
ConvNet Architecture Search for Spatiotemporal Feature LearningCode1
Spatiotemporal Multiplier Networks for Video Action RecognitionCode1
The "something something" video database for learning and evaluating visual common senseCode1
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual ActionsCode1
Quo Vadis, Action Recognition? A New Model and the Kinetics DatasetCode1
Skeleton-based Action Recognition with Convolutional Neural NetworksCode1
Hidden Two-Stream Convolutional Networks for Action RecognitionCode1
Spatiotemporal Residual Networks for Video Action RecognitionCode1
YouTube-8M: A Large-Scale Video Classification BenchmarkCode1
Towards Good Practices for Very Deep Two-Stream ConvNetsCode1
Visual Semantic Role LabelingCode1
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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
4InternVideoTop-1 Accuracy77.2Unverified
5DejaVidTop-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
3OmniVec3-fold Accuracy99.6Unverified
4VideoMAE V2-g3-fold Accuracy99.6Unverified
5BIKE3-fold Accuracy98.8Unverified
6SMART3-fold Accuracy98.64Unverified
7ZeroI2V ViT-L/143-fold Accuracy98.6Unverified
8OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6Unverified
9PERF-Net (multi-distilled S3D)3-fold Accuracy98.6Unverified
10Text4Vis3-fold Accuracy98.2Unverified