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 2451–2500 of 2759 papers

TitleStatusHype
Histogram of Oriented Principal Components for Cross-View Action Recognition—0
Hollywood 3D: Recognizing Actions in 3D Natural Scenes—0
Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding—0
HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition—0
How can objects help action recognition?—0
How Effective are Self-Supervised Models for Contact Identification in Videos—0
How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks—0
Human Action Adverb Recognition: ADHA Dataset and A Three-Stream Hybrid Model—0
Human Action Attribute Learning From Video Data Using Low-Rank Representations—0
Human Action Forecasting by Learning Task Grammars—0
Human Action Performance using Deep Neuro-Fuzzy Recurrent Attention Model—0
Human Action Recognition Across Datasets by Foreground-weighted Histogram Decomposition—0
Human Action Recognition and Assessment via Deep Neural Network Self-Organization—0
Human Action Recognition and Prediction: A Survey—0
Human Action Recognition Based on Context-Dependent Graph Kernels—0
Human Action Recognition Based on Multi-scale Feature Maps from Depth Video Sequences—0
Human Action Recognition Based on Spatial-Temporal Attention—0
Human Action Recognition from Various Data Modalities: A Review—0
Human Action Recognition (HAR) Using Skeleton-based Spatial Temporal Relative Transformer Network: ST-RTR—0
Human Action Recognition in Drone Videos using a Few Aerial Training Examples—0
Human Action Recognition in Egocentric Perspective Using 2D Object and Hands Pose—0
Human Action Recognition in Still Images Using ConViT—0
Human Action Recognition: Pose-based Attention draws focus to Hands—0
Human Action Recognition System using Good Features and Multilayer Perceptron Network—0
Human Action Recognition using Factorized Spatio-Temporal Convolutional Networks—0
Human Action Recognition using Local Two-Stream Convolution Neural Network Features and Support Vector Machines—0
Human action recognition with a large-scale brain-inspired photonic computer—0
Human Action Recognition with Deep Temporal Pyramids—0
Human Action Recognition with Multi-Laplacian Graph Convolutional Networks—0
Human Action Recognition without Human—0
Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework—0
Human activity recognition using deep learning approaches and single frame cnn and convolutional lstm—0
Human activity recognition using improved dynamic image—0
Human and Machine Action Prediction Independent of Object Information—0
Human-Centered Prior-Guided and Task-Dependent Multi-Task Representation Learning for Action Recognition Pre-Training—0
Human-Centric Transformer for Domain Adaptive Action Recognition—0
Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events—0
Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey—0
Human Stone Toolmaking Action Grammar (HSTAG): A Challenging Benchmark for Fine-grained Motor Behavior Recognition—0
HumanVBench: Exploring Human-Centric Video Understanding Capabilities of MLLMs with Synthetic Benchmark Data—0
HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling—0
HumMUSS: Human Motion Understanding using State Space Models—0
HyLiFormer: Hyperbolic Linear Attention for Skeleton-based Human Action Recognition—0
Hyper-Fisher Vectors for Action Recognition—0
Hypergraph-based Multi-View Action Recognition using Event Cameras—0
iCub! Do you recognize what I am doing?: multimodal human action recognition on multisensory-enabled iCub robot—0
Identifying Auxiliary or Adversarial Tasks Using Necessary Condition Analysis for Adversarial Multi-task Video Understanding—0
Identity-free Artificial Emotional Intelligence via Micro-Gesture Understanding—0
IF-TTN: Information Fused Temporal Transformation Network for Video Action Recognition—0
IIP-Transformer: Intra-Inter-Part Transformer for Skeleton-Based 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