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

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Showing 2101–2150 of 2759 papers

TitleStatusHype
MSSTNet: A Multi-Scale Spatio-Temporal CNN-Transformer Network for Dynamic Facial Expression Recognition—0
Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients—0
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition—0
Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking—0
Multi-Dimensional Refinement Graph Convolutional Network with Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition—0
Multidomain Multimodal Fusion For Human Action Recognition Using Inertial Sensors—0
Multi-Expert Human Action Recognition with Hierarchical Super-Class Learning—0
Multi-Feature Max-Margin Hierarchical Bayesian Model for Action Recognition—0
Multi-Fiber Networks for Video Recognition—0
Multi-Grained Feature Pruning for Video-Based Human Pose Estimation—0
Multi-Grained Spatio-Temporal Features Perceived Network for Event-Based Lip-Reading—0
Multi-granularity Generator for Temporal Action Proposal—0
Multi-kernel learning of deep convolutional features for action recognition—0
Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks—0
Multi-Label Zero-Shot Human Action Recognition via Joint Latent Ranking Embedding—0
Multi-Level Recurrent Residual Networks for Action Recognition—0
Multi-manifold Attention for Vision Transformers—0
MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition—0
Multimodal Cross-Domain Few-Shot Learning for Egocentric Action Recognition—0
Multimodal Explanations by Predicting Counterfactuality in Videos—0
Multi-modal Instance Refinement for Cross-domain Action Recognition—0
Multi-modality action recognition based on dual feature shift in vehicle cabin monitoring—0
Multimodal Multipart Learning for Action Recognition in Depth Videos—0
Multimodal Prototype-Enhanced Network for Few-Shot Action Recognition—0
Multi-Modal Three-Stream Network for Action Recognition—0
Multimodal Transformer With a Low-Computational-Cost Guarantee—0
Multi-model learning by sequential reading of untrimmed videos for action recognition—0
Multi-region two-stream R-CNN for action detection—0
Multi-scale Motion-Aware Module for Video Action Recognition—0
Multiscale Multimodal Transformer for Multimodal Action Recognition—0
Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition—0
Multi-Scale Spatial-Temporal Self-Attention Graph Convolutional Networks for Skeleton-based Action Recognition—0
Multi Scale Temporal Graph Networks For Skeleton-based Action Recognition—0
Multi-Stage HRNet: Multiple Stage High-Resolution Network for Human Pose Estimation—0
Multi-Task Clustering of Human Actions by Sharing Information—0
Multitask Learning to Improve Egocentric Action Recognition—0
Multitask Non-Autoregressive Model for Human Motion Prediction—0
Multi-task Sparse Learning with Beta Process Prior for Action Recognition—0
Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation—0
MultiTSF: Transformer-based Sensor Fusion for Human-Centric Multi-view and Multi-modal Action Recognition—0
Multi-velocity neural networks for gesture recognition in videos—0
Multi-view Action Recognition via Directed Gromov-Wasserstein Discrepancy—0
Multiview Cauchy Estimator Feature Embedding for Depth and Inertial Sensor-Based Human Action Recognition—0
Multiview Hessian regularized logistic regression for action recognition—0
Multi-view knowledge distillation transformer for human action recognition—0
Multi-View Region Adaptive Multi-temporal DMM and RGB Action Recognition—0
Multi-View Super Vector for Action Recognition—0
Multiview Transformers for Video Recognition—0
Multi-View Video-Based Learning: Leveraging Weak Labels for Frame-Level Perception—0
Musical Chair: Efficient Real-Time Recognition Using Collaborative IoT Devices—0
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All datasetsSomething-Something V2UCF101HMDB-51Something-Something V1AVA v2.2EPIC-KITCHENS-100NTU RGB+DNTU RGB+D 120Diving-48ActivityNetAVA v2.1H2O (2 Hands and Objects)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MViTv2-B (IN-21K + Kinetics400 pretrain)Top-5 Accuracy93.4—Unverified
2RSANet-R50 (8+16 frames, ImageNet pretrained, 2 clips)Top-5 Accuracy91.1—Unverified
3MVD (Kinetics400 pretrain, ViT-H, 16 frame)Top-1 Accuracy77.3—Unverified
4DejaVidTop-1 Accuracy77.2—Unverified
5InternVideoTop-1 Accuracy77.2—Unverified
6InternVideo2-1BTop-1 Accuracy77.1—Unverified
7VideoMAE V2-gTop-1 Accuracy77—Unverified
8MVD (Kinetics400 pretrain, ViT-L, 16 frame)Top-1 Accuracy76.7—Unverified
9Hiera-L (no extra data)Top-1 Accuracy76.5—Unverified
10TubeViT-LTop-1 Accuracy76.1—Unverified
#ModelMetricClaimedVerifiedStatus
1FTP-UniFormerV2-L/143-fold Accuracy99.7—Unverified
2OmniVec23-fold Accuracy99.6—Unverified
3VideoMAE V2-g3-fold Accuracy99.6—Unverified
4OmniVec3-fold Accuracy99.6—Unverified
5BIKE3-fold Accuracy98.8—Unverified
6SMART3-fold Accuracy98.64—Unverified
7OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6—Unverified
8PERF-Net (multi-distilled S3D)3-fold Accuracy98.6—Unverified
9ZeroI2V ViT-L/143-fold Accuracy98.6—Unverified
10LGD-3D Two-stream3-fold Accuracy98.2—Unverified