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 21512175 of 2759 papers

TitleStatusHype
D3D: Distilled 3D Networks for Video Action RecognitionCode0
Action Machine: Rethinking Action Recognition in Trimmed Videos0
TAN: Temporal Aggregation Network for Dense Multi-label Action Recognition0
Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition with Multimodal TrainingCode0
Nrityantar: Pose oblivious Indian classical dance sequence classification system0
Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition0
Long-Term Feature Banks for Detailed Video UnderstandingCode0
Learning Discriminative Motion Features Through Detection0
Loss Guided Activation for Action Recognition in Still Images0
Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding ManifoldCode0
Video Action Transformer Network0
Multimodal Explanations by Predicting Counterfactuality in Videos0
MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language0
SUSiNet: See, Understand and Summarize it0
A^2-Nets: Double Attention NetworksCode0
Trajectory Convolution for Action Recognition0
Structure-Aware Convolutional Neural NetworksCode0
Graph-Based Global Reasoning NetworksCode0
Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer VisionCode0
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression0
Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction0
Unrepresentative video data: A review and evaluation0
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers0
Multi-granularity Generator for Temporal Action Proposal0
Stacked Spatio-Temporal Graph Convolutional Networks for Action Segmentation0
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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