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 19261950 of 2759 papers

TitleStatusHype
Class Feature Pyramids for Video ExplanationCode0
Multiple Human Tracking using Multi-Cues including Primitive Action FeaturesCode0
Deep Point-wise Prediction for Action Temporal ProposalCode0
ProtoGAN: Towards Few Shot Learning for Action Recognition0
Multitask Learning to Improve Egocentric Action Recognition0
X-ToM: Explaining with Theory-of-Mind for Gaining Justified Human Trust0
Metric-Based Few-Shot Learning for Video Action Recognition0
Adversarial Attack on Skeleton-based Human Action Recognition0
Zero-Shot Action Recognition in Videos: A Survey0
MLGCN: Multi-Laplacian Graph Convolutional Networks for Human Action Recognition0
Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference JointsCode0
Comparative Analysis of CNN-based Spatiotemporal Reasoning in VideosCode0
Video Representation Learning by Dense Predictive CodingCode0
Multi-Modal Three-Stream Network for Action Recognition0
Discriminative Video Representation Learning Using Support Vector Classifiers0
Tensor Analysis with n-Mode Generalized Difference SubspaceCode0
Riemannian batch normalization for SPD neural networks0
EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks0
DWnet: Deep-Wide Network for 3D Action Recognition0
Temporal Reasoning Graph for Activity Recognition0
Cooperative Cross-Stream Network for Discriminative Action Representation0
Mobile Video Action Recognition0
Graph Based Skeleton Modeling for Human Activity Analysis0
Non-local Recurrent Neural Memory for Supervised Sequence Modeling0
EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric 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