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 451475 of 2759 papers

TitleStatusHype
A Multigrid Method for Efficiently Training Video ModelsCode1
Semi-Supervised Action Recognition with Temporal Contrastive LearningCode1
CDFSL-V: Cross-Domain Few-Shot Learning for VideosCode1
Real-time 3D human action recognition based on Hyperpoint sequenceCode1
Side4Video: Spatial-Temporal Side Network for Memory-Efficient Image-to-Video Transfer LearningCode1
Full-Body Articulated Human-Object InteractionCode1
An Action Is Worth Multiple Words: Handling Ambiguity in Action RecognitionCode1
Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the ArtCode1
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action RecognitionCode1
Simba: Mamba augmented U-ShiftGCN for Skeletal Action Recognition in VideosCode1
Ske2Grid: Skeleton-to-Grid Representation Learning for Action RecognitionCode1
Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action RecognitionCode1
CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action RecognitionCode1
Skeleton-DML: Deep Metric Learning for Skeleton-Based One-Shot Action RecognitionCode1
SkeletonX: Data-Efficient Skeleton-based Action Recognition via Cross-sample Feature AggregationCode1
CIAGAN: Conditional Identity Anonymization Generative Adversarial NetworksCode1
CIDEr: Consensus-based Image Description EvaluationCode1
SOAR: Scene-debiasing Open-set Action RecognitionCode1
Source-Free Progressive Graph Learning for Open-Set Domain AdaptationCode1
Space-time Mixing Attention for Video TransformerCode1
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action RecognitionCode1
Skeleton-based Action Recognition via Spatial and Temporal Transformer NetworksCode1
Spatiotemporal Contrastive Video Representation LearningCode1
Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action RecognitionCode1
Deep Multimodal Feature Encoding for Video OrderingCode1
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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
4InternVideoTop-1 Accuracy77.2Unverified
5DejaVidTop-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
3OmniVec3-fold Accuracy99.6Unverified
4VideoMAE V2-g3-fold Accuracy99.6Unverified
5BIKE3-fold Accuracy98.8Unverified
6SMART3-fold Accuracy98.64Unverified
7ZeroI2V ViT-L/143-fold Accuracy98.6Unverified
8OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6Unverified
9PERF-Net (multi-distilled S3D)3-fold Accuracy98.6Unverified
10Text4Vis3-fold Accuracy98.2Unverified