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 12011225 of 2759 papers

TitleStatusHype
Deep Bilinear Learning for RGB-D Action Recognition0
A Survey on Multimodal Wearable Sensor-based Human Action Recognition0
Kernelized Covariance for Action Recognition0
"Knights": First Place Submission for VIPriors21 Action Recognition Challenge at ICCV 20210
A Survey on Human Action Recognition0
Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition0
Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation0
A Survey on Contrastive Self-supervised Learning0
DeepActsNet: Spatial and Motion features from Face, Hands, and Body Combined with Convolutional and Graph Networks for Improved Action Recognition0
Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation0
A Survey on Backbones for Deep Video Action Recognition0
A Survey on 3D Skeleton-Based Action Recognition Using Learning Method0
Actor-Centric Relation Network0
Modular Action Concept Grounding in Semantic Video Prediction0
Decoupled Prompt-Adapter Tuning for Continual Activity Recognition0
Decision Support for Video-based Detection of Flu Symptoms0
A Survey of Visual Analysis of Human Motion and Its Applications0
Deception Detection in Videos0
Improving performance of recurrent neural network with relu nonlinearity0
A Survey of Video-based Action Quality Assessment0
3D R Transform on Spatio-temporal Interest Points for Action Recognition0
Improving Interpretability of Deep Neural Networks with Semantic Information0
Improving Human Action Recognition by Non-action Classification0
DDLSTM: Dual-Domain LSTM for Cross-Dataset Action Recognition0
Improving Zero-Shot Action Recognition using Human Instruction with Text Description0
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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