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 22012225 of 2759 papers

TitleStatusHype
Motion Feature Network: Fixed Motion Filter for Action Recognition0
Contrastive Video Representation Learning via Adversarial Perturbations0
Deep Discriminative Model for Video Classification0
Correlation Net: Spatiotemporal multimodal deep learning for action recognition0
Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot StudyCode1
Signal Alignment for Humanoid Skeletons via the Globally Optimal Reparameterization Algorithm0
Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks0
The Globally Optimal Reparameterization Algorithm: an Alternative to Fast Dynamic Time Warping for Action Recognition in Video Sequences0
Adding Attentiveness to the Neurons in Recurrent Neural Networks0
Video-based Person Re-identification via 3D Convolutional Networks and Non-local Attention0
A Variational Time Series Feature Extractor for Action PredictionCode0
3D Human Action Recognition with Siamese-LSTM Based Deep Metric Learning0
Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization0
Action Recognition for Depth Video using Multi-view Dynamic ImagesCode0
A Novel Geometric Framework on Gram Matrix Trajectories for Human Behavior Understanding0
YH Technologies at ActivityNet Challenge 20180
Human Action Recognition and Prediction: A Survey0
Exploiting Spatial-Temporal Modelling and Multi-Modal Fusion for Human Action Recognition0
CNN-based Action Recognition and Supervised Domain Adaptation on 3D Body Skeletons via Kernel Feature Maps0
Classifying Object Manipulation Actions based on Grasp-types and Motion-Constraints0
Modality Distillation with Multiple Stream Networks for Action RecognitionCode0
Pose Encoding for Robust Skeleton-Based Action Recognition0
Two Stream Self-Supervised Learning for Action Recognition0
Massively Parallel Video Networks0
Action4D: Real-time Action Recognition in the Crowd and Clutter0
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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