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 14011425 of 2759 papers

TitleStatusHype
Continuous Action Recognition Based on Sequence Alignment0
Continuous Human Action Recognition for Human-Machine Interaction: A Review0
Continuous Video to Simple Signals for Swimming Stroke Detection with Convolutional Neural Networks0
Contrast and Order Representations for Video Self-Supervised Learning0
Contrastive Language Video Time Pre-training0
Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency0
Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning0
Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition0
Controllable Attention for Structured Layered Video Decomposition0
Controllable Augmentations for Video Representation Learning0
Convolutional Architecture Exploration for Action Recognition and Image Classification0
Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks0
Cooking in the kitchen: Recognizing and Segmenting Human Activities in Videos0
Cooperative Cross-Stream Network for Discriminative Action Representation0
Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization0
Cooperative Training of Deep Aggregation Networks for RGB-D Action Recognition0
Correlation Net: Spatiotemporal multimodal deep learning for action recognition0
Co-training Transformer with Videos and Images Improves Action Recognition0
Could Giant Pretrained Image Models Extract Universal Representations?0
Coupled Recurrent Network (CRN)0
CPR-Coach: Recognizing Composite Error Actions based on Single-class Training0
Cross-Block Fine-Grained Semantic Cascade for Skeleton-Based Sports Action Recognition0
Cross-Domain First Person Audio-Visual Action Recognition through Relative Norm Alignment0
Cross Domain Model Compression by Structurally Weight Sharing0
Cross-Enhancement Transform Two-Stream 3D ConvNets for 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