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 25012525 of 2759 papers

TitleStatusHype
EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action RecognitionCode0
Ensemble Modeling for Multimodal Visual Action RecognitionCode0
Unsupervised Learning from Video with Deep Neural EmbeddingsCode0
Action Recognition based on Cross-Situational Action-object StatisticsCode0
Spatiotemporal Action Recognition in Restaurant VideosCode0
Towards a geometric understanding of Spatio Temporal Graph Convolution NetworksCode0
An Animation-based Augmentation Approach for Action Recognition from Discontinuous VideoCode0
Analysis of Hand Segmentation in the WildCode0
Beyond the Self: Using Grounded Affordances to Interpret and Describe Others' ActionsCode0
Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM NetworksCode0
Unsupervised Learning of View-invariant Action RepresentationsCode0
Precondition and Effect Reasoning for Action RecognitionCode0
Learning Skeletal Graph Neural Networks for Hard 3D Pose EstimationCode0
Zero-Shot Action Recognition from Diverse Object-Scene CompositionsCode0
Unsupervised Motion Representation Learning with Capsule AutoencodersCode0
Learning Mutual Excitation for Hand-to-Hand and Human-to-Human Interaction RecognitionCode0
Learning long-term dependencies for action recognition with a biologically-inspired deep networkCode0
Unsupervised Representation Learning by Sorting SequencesCode0
End-to-End Semantic Video Transformer for Zero-Shot Action RecognitionCode0
Pre-training for Action Recognition with Automatically Generated Fractal DatasetsCode0
Beyond the Benchmark: Detecting Diverse Anomalies in VideosCode0
Beyond Short Snippets: Deep Networks for Video ClassificationCode0
Learning Human Action Recognition Representations Without Real HumansCode0
Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural SearchingCode0
Bayesian Hierarchical Dynamic Model for Human Action RecognitionCode0
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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