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 1801–1850 of 2759 papers

TitleStatusHype
Action Recognition With Motion Diversification and Dynamic Selection—0
Action Recognition with Spatio-Temporal Visual Attention on Skeleton Image Sequences—0
Action Representation Using Classifier Decision Boundaries—0
Actions in the Eye: Dynamic Gaze Datasets and Learnt Saliency Models for Visual Recognition—0
Action Understanding with Multiple Classes of Actors—0
ActionXPose: A Novel 2D Multi-view Pose-based Algorithm for Real-time Human Action Recognition—0
Active Exploration of Multimodal Complementarity for Few-Shot Action Recognition—0
Active Generation Network of Human Skeleton for Action Recognition—0
Active Multimodal Distillation for Few-shot Action Recognition—0
Activity Recognition on a Large Scale in Short Videos - Moments in Time Dataset—0
Actor-Centric Relation Network—0
SkelVIT: Consensus of Vision Transformers for a Lightweight Skeleton-Based Action Recognition System—0
Towards Weakly Supervised End-to-end Learning for Long-video Action Recognition—0
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition—0
AdapNet: Adaptability Decomposing Encoder-Decoder Network for Weakly Supervised Action Recognition and Localization—0
Adapting Vision-Language Models for Evaluating World Models—0
Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures—0
Adaptive Hyper-Graph Convolution Network for Skeleton-based Human Action Recognition with Virtual Connections—0
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition—0
Adaptive Recursive Circle Framework for Fine-grained Action Recognition—0
Adaptive RNN Tree for Large-Scale Human Action Recognition—0
AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos—0
AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition—0
Adding Attentiveness to the Neurons in Recurrent Neural Networks—0
A Deep Learning Approach for Real-Time 3D Human Action Recognition from Skeletal Data—0
ADHD diagnosis based on action characteristics recorded in videos using machine learning—0
A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D dataset—0
Advances in Human Action Recognition: A Survey—0
Adversarial Attack on Skeleton-based Human Action Recognition—0
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers—0
Adversarial Bone Length Attack on Action Recognition—0
Adversarial Cross-Domain Action Recognition with Co-Attention—0
Adversarial Domain Adaptation for Action Recognition Around the Clock—0
Adversarial Robustness in RGB-Skeleton Action Recognition: Leveraging Attention Modality Reweighter—0
Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition—0
AdvIT: Adversarial Frames Identifier Based on Temporal Consistency in Videos—0
AE-Net:Adjoint Enhancement Network for Efficient Action Recognition in Video Understanding—0
AFE-CNN: 3D Skeleton-based Action Recognition with Action Feature Enhancement—0
A Fine-to-Coarse Convolutional Neural Network for 3D Human Action Recognition—0
A Generative Approach to Zero-Shot and Few-Shot Action Recognition—0
A Generative Restricted Boltzmann Machine Based Method for High-Dimensional Motion Data Modeling—0
A Grid-based Representation for Human Action Recognition—0
A Hierarchical Context Model for Event Recognition in Surveillance Video—0
A Hierarchical Graph-based Approach for Recognition and Description Generation of Bimanual Actions in Videos—0
A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets—0
A Hybrid Framework for Action Recognition in Low-Quality Video Sequences—0
A Hybrid Loss for Multiclass and Structured Prediction—0
A Hybrid RNN-HMM Approach for Weakly Supervised Temporal Action Segmentation—0
AI on the Road: A Comprehensive Analysis of Traffic Accidents and Accident Detection System in Smart Cities—0
A Key Volume Mining Deep Framework for Action Recognition—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