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 501525 of 2759 papers

TitleStatusHype
Context-Aware RCNN: A Baseline for Action Detection in VideosCode1
Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action RecognitionCode1
M^2DAR: Multi-View Multi-Scale Driver Action Recognition with Vision TransformerCode1
Training a Large Video Model on a Single Machine in a DayCode1
Transformer-Based Unified Recognition of Two Hands Manipulating ObjectsCode1
DDGCN: A Dynamic Directed Graph Convolutional Network for Action RecognitionCode1
Location-aware Graph Convolutional Networks for Video Question AnsweringCode1
Complex Sequential Understanding through the Awareness of Spatial and Temporal ConceptsCode1
Can An Image Classifier Suffice For Action Recognition?Code1
Look More but Care Less in Video RecognitionCode1
Animal Kingdom: A Large and Diverse Dataset for Animal Behavior UnderstandingCode1
EgoNCE++: Do Egocentric Video-Language Models Really Understand Hand-Object Interactions?Code1
Constructing Stronger and Faster Baselines for Skeleton-based Action RecognitionCode1
An Image is Worth 16x16 Words, What is a Video Worth?Code1
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action RecognitionCode1
Compressing Recurrent Neural Networks with Tensor Ring for Action RecognitionCode1
Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video DatasetCode1
Make Skeleton-based Action Recognition Model Smaller, Faster and BetterCode1
Concatenated Masked Autoencoders as Spatial-Temporal LearnerCode1
Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action RecognitionCode1
ConvNet Architecture Search for Spatiotemporal Feature LearningCode1
Masked Motion Predictors are Strong 3D Action Representation LearnersCode1
Volterra Neural Networks (VNNs)Code1
Conquering the cnn over-parameterization dilemma: A volterra filtering approach for action recognitionCode1
M2A: Motion Aware Attention for Accurate Video Action RecognitionCode1
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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