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 851–900 of 2759 papers

TitleStatusHype
Learning to Estimate Pose by Watching VideosCode0
Deep Point-wise Prediction for Action Temporal ProposalCode0
Attack on Scene Flow using Point CloudsCode0
Attack-Augmentation Mixing-Contrastive Skeletal Representation LearningCode0
Learning Spatio-Temporal Features with 3D Residual Networks for Action RecognitionCode0
Learning Skeletal Graph Neural Networks for Hard 3D Pose EstimationCode0
EV-Action: Electromyography-Vision Multi-Modal Action DatasetCode0
Learning Spatio-Temporal Representation with Local and Global DiffusionCode0
Let's Dance: Learning From Online Dance VideosCode0
Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural SearchingCode0
Learning Gating ConvNet for Two-Stream based Methods in Action RecognitionCode0
Learning Human Action Recognition Representations Without Real HumansCode0
Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action RecognitionCode0
Learning from Video and Text via Large-Scale Discriminative ClusteringCode0
Learning long-term dependencies for action recognition with a biologically-inspired deep networkCode0
Learning deep representations for video-based intake gesture detectionCode0
Learning Actor Relation Graphs for Group Activity RecognitionCode0
Every Moment Counts: Dense Detailed Labeling of Actions in Complex VideosCode0
Large-scale weakly-supervised pre-training for video action recognitionCode0
Asynchronous Temporal Fields for Action RecognitionCode0
Asymmetric Masked Distillation for Pre-Training Small Foundation ModelsCode0
DeepGRU: Deep Gesture Recognition UtilityCode0
Actor-identified Spatiotemporal Action Detection --- Detecting Who Is Doing What in VideosCode0
Language Model Guided Interpretable Video Action ReasoningCode0
Large-scale Robustness Analysis of Video Action Recognition ModelsCode0
Excitation Backprop for RNNsCode0
Kronecker Mask and Interpretive Prompts are Language-Action Video LearnersCode0
SkateboardAI: The Coolest Video Action Recognition for SkateboardingCode0
Learning Mutual Excitation for Hand-to-Hand and Human-to-Human Interaction RecognitionCode0
Joint Mixing Data Augmentation for Skeleton-based Action RecognitionCode0
Cross-Model Cross-Stream Learning for Self-Supervised Human Action RecognitionCode0
Joint Discovery of Object States and Manipulation ActionsCode0
Joint-Partition Group Attention for skeleton-based action recognitionCode0
Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer VisionCode0
Skeleton-Based Action Recognition With Directed Graph Neural NetworksCode0
Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional NetworksCode0
JOSENet: A Joint Stream Embedding Network for Violence Detection in Surveillance VideosCode0
Interpretable 3D Human Action Analysis with Temporal Convolutional NetworksCode0
Actor and Observer: Joint Modeling of First and Third-Person VideosCode0
DEAR: Depth-Enhanced Action RecognitionCode0
Investigation of Different Skeleton Features for CNN-based 3D Action RecognitionCode0
2D Pose Estimation based Child Action RecognitionCode0
DD-GCN: Directed Diffusion Graph Convolutional Network for Skeleton-based Human Action RecognitionCode0
Exploring Modulated Detection Transformer as a Tool for Action Recognition in VideosCode0
ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised Action Recognition in VideosCode0
Interaction Relational Network for Mutual Action RecognitionCode0
ActivityNet: A Large-Scale Video Benchmark for Human Activity UnderstandingCode0
Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural ActivitiesCode0
In My Perspective, In My Hands: Accurate Egocentric 2D Hand Pose and Action RecognitionCode0
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video ArchitecturesCode0
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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
4InternVideoTop-1 Accuracy77.2—Unverified
5DejaVidTop-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
3OmniVec3-fold Accuracy99.6—Unverified
4VideoMAE V2-g3-fold Accuracy99.6—Unverified
5BIKE3-fold Accuracy98.8—Unverified
6SMART3-fold Accuracy98.64—Unverified
7ZeroI2V ViT-L/143-fold Accuracy98.6—Unverified
8OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6—Unverified
9PERF-Net (multi-distilled S3D)3-fold Accuracy98.6—Unverified
10Text4Vis3-fold Accuracy98.2—Unverified