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 24762500 of 2759 papers

TitleStatusHype
Efficient Action Detection in Untrimmed Videos via Multi-Task Learning0
Deep Motion Features for Visual Tracking0
Dynamic Action Recognition: A convolutional neural network model for temporally organized joint location data0
Asynchronous Temporal Fields for Action RecognitionCode0
Deep Learning on Lie Groups for Skeleton-based Action Recognition0
Single Image Action Recognition using Semantic Body Part Actions0
Scale Coding Bag of Deep Features for Human Attribute and Action Recognition0
ActionFlowNet: Learning Motion Representation for Action Recognition0
Procedural Generation of Videos to Train Deep Action Recognition Networks0
Action Recognition with Dynamic Image NetworksCode0
Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition0
Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs0
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model0
Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition0
Action Recognition Based on Optimal Joint Selection and Discriminative Depth DescriptorCode0
Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation0
AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos0
Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures0
Learning Multi-level Features For Sensor-based Human Action Recognition0
Self-Supervised Video Representation Learning With Odd-One-Out Networks0
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data0
Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation0
Joint Network based Attention for Action Recognition0
Learning long-term dependencies for action recognition with a biologically-inspired deep networkCode0
Learning To Score Olympic EventsCode0
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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