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 1151–1200 of 2759 papers

TitleStatusHype
Joint Max Margin and Semantic Features for Continuous Event Detection in Complex Scenes—0
Joint Recognition and Segmentation of Actions via Probabilistic Integration of Spatio-Temporal Fisher Vectors—0
Deep-Temporal LSTM for Daily Living Action Recognition—0
Deep Spatio-temporal Manifold Network for Action Recognition—0
Attention-based Temporal Weighted Convolutional Neural Network for Action Recognition—0
Deep set conditioned latent representations for action recognition—0
ActionFlowNet: Learning Motion Representation for Action Recognition—0
Deep Sequential Context Networks for Action Prediction—0
Adapting Vision-Language Models for Evaluating World Models—0
3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Naïve—0
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model—0
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition—0
Attend and Interact: Higher-Order Object Interactions for Video Understanding—0
Joint Discriminative Bayesian Dictionary and Classifier Learning—0
Self-Supervised Joint Encoding of Motion and Appearance for First Person Action Recognition—0
Action Detection by Implicit Intentional Motion Clustering—0
Deep Neural Networks in Video Human Action Recognition: A Review—0
2D versus 3D Convolutional Spiking Neural Networks Trained with Unsupervised STDP for Human Action Recognition—0
Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos—0
Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition—0
Deep Multi-Kernel Convolutional LSTM Networks and an Attention-Based Mechanism for Videos—0
Deep Motion Features for Visual Tracking—0
ATTACH Dataset: Annotated Two-Handed Assembly Actions for Human Action Understanding—0
Deep manifold-to-manifold transforming network for action recognition—0
Deep Local Video Feature for Action Recognition—0
A Training Method For VideoPose3D With Ideology of Action Recognition—0
AdapNet: Adaptability Decomposing Encoder-Decoder Network for Weakly Supervised Action Recognition and Localization—0
Joint Denoising / Compression of Image Contours via Shape Prior and Context Tree—0
Joint Image-Instance Spatial-Temporal Attention for Few-shot Action Recognition—0
Joint Temporal Pooling for Improving Skeleton-based Action Recognition—0
Kernelized Covariance for Action Recognition—0
LS-HAR: Language Supervised Human Action Recognition with Salient Fusion, Construction Sites as a Use-Case—0
Deep Learning Techniques for Video Instance Segmentation: A Survey—0
Deep Learning on Lie Groups for Skeleton-based Action Recognition—0
AtGCN: A Graph Convolutional Network For Ataxic Gait Detection—0
Deep Learning for Vision-Based Fall Detection System: Enhanced Optical Dynamic Flow—0
Deep Learning Computer Vision Algorithms for Real-time UAVs On-board Camera Image Processing—0
A Temporal Sequence Learning for Action Recognition and Prediction—0
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition—0
Deep learning-based approaches for human motion decoding in smart walkers for rehabilitation—0
Deep Learning-based Action Detection in Untrimmed Videos: A Survey—0
A Temporal Fusion Approach for Video Classification with Convolutional and LSTM Neural Networks Applied to Violence Detection—0
Deep Learning Approaches for Human Action Recognition in Video Data—0
Deep Image-to-Video Adaptation and Fusion Networks for Action Recognition—0
Towards Weakly Supervised End-to-end Learning for Long-video Action Recognition—0
Action Detail Matters: Refining Video Recognition with Local Action Queries—0
Is Temporal Prompting All We Need For Limited Labeled Action Recognition?—0
Deep hierarchical pooling design for cross-granularity action recognition—0
SkelVIT: Consensus of Vision Transformers for a Lightweight Skeleton-Based Action Recognition System—0
3D Skeleton-Based Action Recognition: A Review—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