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 2401–2450 of 2759 papers

TitleStatusHype
Learning without Prejudice: Avoiding Bias in Webly-Supervised Action Recognition—0
The "something something" video database for learning and evaluating visual common senseCode1
Joint Max Margin and Semantic Features for Continuous Event Detection in Complex Scenes—0
Learning to Learn from Noisy Web Videos—0
Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition—0
Deep manifold-to-manifold transforming network for action recognition—0
Continuous Video to Simple Signals for Swimming Stroke Detection with Convolutional Neural Networks—0
Sequence Summarization Using Order-constrained Kernelized Feature Subspaces—0
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural NetworksCode0
Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action Recognition—0
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual ActionsCode1
Quo Vadis, Action Recognition? A New Model and the Kinetics DatasetCode1
Learning Spatiotemporal Features for Infrared Action Recognition with 3D Convolutional Neural Networks—0
Single Image Action Recognition by Predicting Space-Time Saliency—0
An Improved Video Analysis using Context based Extension of LSH—0
Deep Spatio-temporal Manifold Network for Action Recognition—0
CHAM: action recognition using convolutional hierarchical attention model—0
Temporal Segment Networks for Action Recognition in VideosCode2
Investigation of Different Skeleton Features for CNN-based 3D Action RecognitionCode0
Joint Denoising / Compression of Image Contours via Shape Prior and Context Tree—0
Action Understanding with Multiple Classes of Actors—0
Skeleton-based Action Recognition with Convolutional Neural NetworksCode1
An Analysis of Action Recognition Datasets for Language and Vision Tasks—0
Body Joint guided 3D Deep Convolutional Descriptors for Action Recognition—0
Second-order Temporal Pooling for Action Recognition—0
Context-based Object Viewpoint Estimation: A 2D Relational Approach—0
Invariant recognition drives neural representations of action sequences—0
Temporal Action Detection with Structured Segment NetworksCode2
Skeleton Boxes: Solving skeleton based action detection with a single deep convolutional neural network—0
Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep cnn—0
Interpretable 3D Human Action Analysis with Temporal Convolutional NetworksCode0
Learning to Estimate Pose by Watching VideosCode0
UC Merced Submission to the ActivityNet Challenge 2016—0
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks—0
First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose AnnotationsCode0
Generalized Rank Pooling for Activity Recognition—0
Action Representation Using Classifier Decision Boundaries—0
Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition—0
Unsupervised Action Proposal Ranking through Proposal Recombination—0
Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and DetectionCode0
Hidden Two-Stream Convolutional Networks for Action RecognitionCode1
Skeletonnet: Mining deep part features for 3-d action recognition—0
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity RecognitionCode0
Pose-conditioned Spatio-Temporal Attention for Human Action Recognition—0
Learning and Refining of Privileged Information-based RNNs for Action Recognition from Depth Sequences—0
Locality preserving projection on SPD matrix Lie group: algorithm and analysis—0
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton DataCode0
A Bag-of-Words Equivalent Recurrent Neural Network for Action RecognitionCode0
Two-Stream RNN/CNN for Action Recognition in 3D Videos—0
PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding—0
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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