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
Cycle-Contrast for Self-Supervised Video Representation Learning—0
DARE: AI-based Diver Action Recognition System using Multi-Channel CNNs for AUV Supervision—0
Dark Transformer: A Video Transformer for Action Recognition in the Dark—0
Data Collection-free Masked Video Modeling—0
Data-Folding and Hyperspace Coding for Multi-Dimensonal Time-Series Data Imaging—0
DAVE: Diverse Atomic Visual Elements Dataset with High Representation of Vulnerable Road Users in Complex and Unpredictable Environments—0
DDLSTM: Dual-Domain LSTM for Cross-Dataset Action Recognition—0
Deception Detection in Videos—0
Decision Support for Video-based Detection of Flu Symptoms—0
Decoupled Prompt-Adapter Tuning for Continual Activity Recognition—0
Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation—0
DeepActsNet: Spatial and Motion features from Face, Hands, and Body Combined with Convolutional and Graph Networks for Improved Action Recognition—0
Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation—0
Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition—0
Deep Bilinear Learning for RGB-D Action Recognition—0
Deep Convolutional Neural Networks for Action Recognition Using Depth Map Sequences—0
Deep Discriminative Model for Video Classification—0
DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization—0
DeepFN: Towards Generalizable Facial Action Unit Recognition with Deep Face Normalization—0
Deep Graph Reprogramming—0
Deep hierarchical pooling design for cross-granularity action recognition—0
Deep Image-to-Video Adaptation and Fusion Networks for Action Recognition—0
Deep Learning Approaches for Human Action Recognition in Video Data—0
Deep Learning-based Action Detection in Untrimmed Videos: A Survey—0
Deep learning-based approaches for human motion decoding in smart walkers for rehabilitation—0
Deep Learning Computer Vision Algorithms for Real-time UAVs On-board Camera Image Processing—0
Deep Learning for Vision-Based Fall Detection System: Enhanced Optical Dynamic Flow—0
Deep Learning on Lie Groups for Skeleton-based Action Recognition—0
Deep Learning Techniques for Video Instance Segmentation: A Survey—0
Deep Local Video Feature for Action Recognition—0
Deep manifold-to-manifold transforming network for action recognition—0
Deep Motion Features for Visual Tracking—0
Deep Multi-Kernel Convolutional LSTM Networks and an Attention-Based Mechanism for Videos—0
Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos—0
Deep Neural Networks in Video Human Action Recognition: A Review—0
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition—0
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model—0
Deep Sequential Context Networks for Action Prediction—0
Deep set conditioned latent representations for action recognition—0
Deep Spatio-temporal Manifold Network for Action Recognition—0
Deep-Temporal LSTM for Daily Living Action Recognition—0
Deep-VFX: Deep Action Recognition Driven VFX for Short Video—0
Defending Multimodal Fusion Models against Single-Source Adversaries—0
Deformable Tube Network for Action Detection in Videos—0
Delta Sampling R-BERT for limited data and low-light action recognition—0
Delving Into Egocentric Actions—0
DENOISER: Rethinking the Robustness for Open-Vocabulary Action Recognition—0
Density-Guided Label Smoothing for Temporal Localization of Driving Actions—0
Depth2Action: Exploring Embedded Depth for Large-Scale Action Recognition—0
Depth and Skeleton Associated Action Recognition without Online Accessible RGB-D Cameras—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