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 1501–1550 of 2759 papers

TitleStatusHype
Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution—0
Differentiable Frequency-based Disentanglement for Aerial Video Action Recognition—0
Differential Recurrent Neural Networks for Action Recognition—0
Clustering and Recognition of Spatiotemporal Features through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Direct Dense Pose Estimation—0
Directional Temporal Modeling for Action Recognition—0
Direct Motion Models for Assessing Generated Videos—0
Discriminability Distillation in Group Representation Learning—0
Discriminative Bayesian Dictionary Learning for Classification—0
Discriminative convolutional Fisher vector network for action recognition—0
Discriminative Dictionary Design for Action Classification in Still Images and Videos—0
Discriminative Hierarchical Rank Pooling for Activity Recognition—0
Discriminatively Trained Latent Ordinal Model for Video Classification—0
Discriminative Video Representation Learning Using Support Vector Classifiers—0
DiscrimNet: Semi-Supervised Action Recognition from Videos using Generative Adversarial Networks—0
Disentangled Action Recognition with Knowledge Bases—0
Distantly Supervised Semantic Text Detection and Recognition for Broadcast Sports Videos Understanding—0
StableMamba: Distillation-free Scaling of Large SSMs for Images and Videos—0
Distillation of Human-Object Interaction Contexts for Action Recognition—0
Distilling Knowledge from CNN-Transformer Models for Enhanced Human Action Recognition—0
DistInit: Learning Video Representations Without a Single Labeled Video—0
Distributed non-parametric deep and wide networks—0
Distribution of Action Movements (DAM): A Descriptor for Human Action Recognition—0
Dividing and Aggregating Network for Multi-view Action Recognition—0
DIY Human Action Data Set Generation—0
DL-KDD: Dual-Light Knowledge Distillation for Action Recognition in the Dark—0
DL-SFA: Deeply-Learned Slow Feature Analysis for Action Recognition—0
DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition—0
DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition—0
DMSD-CDFSAR: Distillation from Mixed-Source Domain for Cross-Domain Few-shot Action Recognition—0
DOAD: Decoupled One Stage Action Detection Network—0
Do Less and Achieve More: Training CNNs for Action Recognition Utilizing Action Images from the Web—0
Domain Generalization: A Survey—0
Domain Generalization through Audio-Visual Relative Norm Alignment in First Person Action Recognition—0
Domain-Specific Priors and Meta Learning for Few-Shot First-Person Action Recognition—0
Dominant Codewords Selection with Topic Model for Action Recognition—0
Down-Sampling coupled to Elastic Kernel Machines for Efficient Recognition of Isolated Gestures—0
Drive&Act: A Multi-Modal Dataset for Fine-Grained Driver Behavior Recognition in Autonomous Vehicles—0
Driver Activity Classification Using Generalizable Representations from Vision-Language Models—0
DTG-Net: Differentiated Teachers Guided Self-Supervised Video Action Recognition—0
DWnet: Deep-Wide Network for 3D Action Recognition—0
Dynamic Action Recognition: A convolutional neural network model for temporally organized joint location data—0
Dynamically Encoded Actions Based on Spacetime Saliency—0
Dynamic Appearance: A Video Representation for Action Recognition with Joint Training—0
Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition—0
Dynamic Hypergraph Convolutional Networks for Skeleton-Based Action Recognition—0
Dynamic Inference: A New Approach Toward Efficient Video Action Recognition—0
Dynamic Matrix Decomposition for Action Recognition—0
Dynamic Probabilistic Network Based Human 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