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 11261150 of 2759 papers

TitleStatusHype
FlowCaps: Optical Flow Estimation with Capsule Networks For Action Recognition0
Flip-Invariant Motion Representation0
CNN-based Action Recognition and Supervised Domain Adaptation on 3D Body Skeletons via Kernel Feature Maps0
An Attention-Enhanced Recurrent Graph Convolutional Network for Skeleton-Based Action Recognition0
Flatten: Video Action Recognition is an Image Classification task0
Fitting, Comparison, and Alignment of Trajectories on Positive Semi-Definite Matrices with Application to Action Recognition0
CNN-Based Action Recognition and Pose Estimation for Classifying Animal Behavior from Videos: A Survey0
An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition0
Action Recognition Using Supervised Spiking Neural Networks0
A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions0
3D Human motion anticipation and classification0
CMAE-V: Contrastive Masked Autoencoders for Video Action Recognition0
First-Take-All: Temporal Order-Preserving Hashing for 3D Action Videos0
CM2-Net: Continual Cross-Modal Mapping Network for Driver Action Recognition0
An Approach to Pose-Based Action Recognition0
First Person Action Recognition Using Deep Learned Descriptors0
FinePseudo: Improving Pseudo-Labelling through Temporal-Alignablity for Semi-Supervised Fine-Grained Action Recognition0
CLTA: Contents and Length-based Temporal Attention for Few-shot Action Recognition0
Action Recognition in Video Using Sparse Coding and Relative Features0
FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding0
Fine-grained Video Categorization with Redundancy Reduction Attention0
Clean Text and Full-Body Transformer: Microsoft's Submission to the WMT22 Shared Task on Sign Language Translation0
Fine-Grained Side Information Guided Dual-Prompts for Zero-Shot Skeleton Action Recognition0
Fine-grained Multi-Modal Self-Supervised Learning0
CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition0
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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