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

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Showing 2101–2150 of 2759 papers

TitleStatusHype
Spatio-temporal Action Recognition: A Survey—0
DistInit: Learning Video Representations Without a Single Labeled Video—0
ODN: Opening the Deep Network for Open-set Action Recognition—0
Skeleton-based Action Recognition of People Handling Objects—0
Semantic Image Networks for Human Action Recognition—0
DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition—0
UAV-GESTURE: A Dataset for UAV Control and Gesture RecognitionCode0
Mutual Context Network for Jointly Estimating Egocentric Gaze and Actions—0
Action2Vec: A Crossmodal Embedding Approach to Action Learning—0
Cross-modal Learning by Hallucinating Missing Modalities in RGB-D VisionCode0
Learning to Recognize 3D Human Action from A New Skeleton-based Representation Using Deep Convolutional Neural Networks—0
Coupled Recurrent Network (CRN)—0
Temporal Hockey Action Recognition via Pose and Optical Flows—0
Canonical Correlation Analysis for Misaligned Satellite Image Change Detection—0
D3D: Distilled 3D Networks for Video Action RecognitionCode0
Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition with Multimodal TrainingCode0
TAN: Temporal Aggregation Network for Dense Multi-label Action Recognition—0
Action Machine: Rethinking Action Recognition in Trimmed Videos—0
Nrityantar: Pose oblivious Indian classical dance sequence classification system—0
Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition—0
Long-Term Feature Banks for Detailed Video UnderstandingCode0
Loss Guided Activation for Action Recognition in Still Images—0
Learning Discriminative Motion Features Through Detection—0
SlowFast Networks for Video RecognitionCode1
Video Action Transformer Network—0
Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding ManifoldCode0
Multimodal Explanations by Predicting Counterfactuality in Videos—0
Timeception for Complex Action RecognitionCode1
MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language—0
SUSiNet: See, Understand and Summarize it—0
Trajectory Convolution for Action Recognition—0
A^2-Nets: Double Attention NetworksCode0
Structure-Aware Convolutional Neural NetworksCode0
Graph-Based Global Reasoning NetworksCode0
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression—0
Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer VisionCode0
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers—0
Unrepresentative video data: A review and evaluation—0
Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction—0
Multi-granularity Generator for Temporal Action Proposal—0
Evolving Space-Time Neural Architectures for Videos—0
LSTA: Long Short-Term Attention for Egocentric Action RecognitionCode0
Stacked Spatio-Temporal Graph Convolutional Networks for Action Segmentation—0
Temporal Bilinear Networks for Video Action Recognition—0
Learning Conditional Random Fields with Augmented Observations for Partially Observed Action Recognition—0
Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images—0
Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles—0
RGB-D Based Action Recognition with Light-weight 3D Convolutional Networks—0
Multi-Task Learning of Generalizable Representations for Video Action Recognition—0
TSM: Temporal Shift Module for Efficient Video UnderstandingCode1
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All datasetsSomething-Something V2UCF101HMDB-51Something-Something V1AVA v2.2EPIC-KITCHENS-100NTU RGB+DNTU RGB+D 120Diving-48ActivityNetAVA v2.1H2O (2 Hands and Objects)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MViTv2-B (IN-21K + Kinetics400 pretrain)Top-5 Accuracy93.4—Unverified
2RSANet-R50 (8+16 frames, ImageNet pretrained, 2 clips)Top-5 Accuracy91.1—Unverified
3MVD (Kinetics400 pretrain, ViT-H, 16 frame)Top-1 Accuracy77.3—Unverified
4DejaVidTop-1 Accuracy77.2—Unverified
5InternVideoTop-1 Accuracy77.2—Unverified
6InternVideo2-1BTop-1 Accuracy77.1—Unverified
7VideoMAE V2-gTop-1 Accuracy77—Unverified
8MVD (Kinetics400 pretrain, ViT-L, 16 frame)Top-1 Accuracy76.7—Unverified
9Hiera-L (no extra data)Top-1 Accuracy76.5—Unverified
10TubeViT-LTop-1 Accuracy76.1—Unverified
#ModelMetricClaimedVerifiedStatus
1FTP-UniFormerV2-L/143-fold Accuracy99.7—Unverified
2OmniVec23-fold Accuracy99.6—Unverified
3VideoMAE V2-g3-fold Accuracy99.6—Unverified
4OmniVec3-fold Accuracy99.6—Unverified
5BIKE3-fold Accuracy98.8—Unverified
6SMART3-fold Accuracy98.64—Unverified
7OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6—Unverified
8PERF-Net (multi-distilled S3D)3-fold Accuracy98.6—Unverified
9ZeroI2V ViT-L/143-fold Accuracy98.6—Unverified
10LGD-3D Two-stream3-fold Accuracy98.2—Unverified