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 14261450 of 2759 papers

TitleStatusHype
BASAR:Black-box Attack on Skeletal Action RecognitionCode1
Understanding the Robustness of Skeleton-based Action Recognition under Adversarial AttackCode1
VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning ChallengesCode1
Unsupervised Motion Representation Enhanced Network for Action Recognition0
DeepFN: Towards Generalizable Facial Action Unit Recognition with Deep Face Normalization0
Domain Generalization: A Survey0
Domain and View-point Agnostic Hand Action RecognitionCode0
A Body Part Embedding Model With Datasets for Measuring 2D Human Motion SimilarityCode1
Learning Transferable Visual Models From Natural Language SupervisionCode2
Phase Space Reconstruction Network for Lane Intrusion Action Recognition0
A Temporal Fusion Approach for Video Classification with Convolutional and LSTM Neural Networks Applied to Violence Detection0
Self-Supervised Learning via multi-Transformation Classification for Action Recognition0
One-shot action recognition in challenging therapy scenariosCode1
Learning to Recognize Actions on Objects in Egocentric Video with Attention Dictionaries0
Win-Fail Action RecognitionCode0
Learning Self-Similarity in Space and Time as Generalized Motion for Video Action RecognitionCode1
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition0
Negative Data AugmentationCode1
Is Space-Time Attention All You Need for Video Understanding?Code2
Video Action Recognition Using spatio-temporal optical flow video frames0
Semi-Supervised Action Recognition with Temporal Contrastive LearningCode1
GCF-Net: Gated Clip Fusion Network for Video Action Recognition0
Optical flow and scene flow estimation: A survey0
Video Transformer NetworkCode0
Embedding Symbolic Temporal Knowledge into Deep Sequential Models0
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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