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 21012125 of 2759 papers

TitleStatusHype
MSSTNet: A Multi-Scale Spatio-Temporal CNN-Transformer Network for Dynamic Facial Expression Recognition0
Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients0
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition0
Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking0
Multi-Dimensional Refinement Graph Convolutional Network with Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition0
Multidomain Multimodal Fusion For Human Action Recognition Using Inertial Sensors0
Multi-Expert Human Action Recognition with Hierarchical Super-Class Learning0
Multi-Feature Max-Margin Hierarchical Bayesian Model for Action Recognition0
Multi-Fiber Networks for Video Recognition0
Multi-Grained Feature Pruning for Video-Based Human Pose Estimation0
Multi-Grained Spatio-Temporal Features Perceived Network for Event-Based Lip-Reading0
Multi-granularity Generator for Temporal Action Proposal0
Multi-kernel learning of deep convolutional features for action recognition0
Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks0
Multi-Label Zero-Shot Human Action Recognition via Joint Latent Ranking Embedding0
Multi-Level Recurrent Residual Networks for Action Recognition0
Multi-manifold Attention for Vision Transformers0
MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition0
Multimodal Cross-Domain Few-Shot Learning for Egocentric Action Recognition0
Multimodal Explanations by Predicting Counterfactuality in Videos0
Multi-modal Instance Refinement for Cross-domain Action Recognition0
Multi-modality action recognition based on dual feature shift in vehicle cabin monitoring0
Multimodal Multipart Learning for Action Recognition in Depth Videos0
Multimodal Prototype-Enhanced Network for Few-Shot Action Recognition0
Multi-Modal Three-Stream Network 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