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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2551–2600 of 2759 papers

TitleStatusHype
Joint Learning On The Hierarchy Representation for Fine-Grained Human Action Recognition—0
Joint Max Margin and Semantic Features for Continuous Event Detection in Complex Scenes—0
Joint Network based Attention for Action Recognition—0
Joint Recognition and Segmentation of Actions via Probabilistic Integration of Spatio-Temporal Fisher Vectors—0
Joint Temporal Pooling for Improving Skeleton-based Action Recognition—0
Joint Visual-Temporal Embedding for Unsupervised Learning of Actions in Untrimmed Sequences—0
JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition—0
JUMPS: Joints Upsampling Method for Pose Sequences—0
Just Add ?! Pose Induced Video Transformers for Understanding Activities of Daily Living—0
Just One Moment: Structural Vulnerability of Deep Action Recognition against One Frame Attack—0
KAN-HyperpointNet for Point Cloud Sequence-Based 3D Human Action Recognition—0
Kernelized Covariance for Action Recognition—0
Kinematic-Layout-aware Random Forests for Depth-based Action Recognition—0
"Knights": First Place Submission for VIPriors21 Action Recognition Challenge at ICCV 2021—0
Knowing What, Where and When to Look: Efficient Video Action Modeling with Attention—0
Knowledge Distillation for Multimodal Egocentric Action Recognition Robust to Missing Modalities—0
Knowledge Fusion Transformers for Video Action Recognition—0
Knowledge Guided Learning: Towards Open Domain Egocentric Action Recognition with Zero Supervision—0
Knowledge Integration Networks for Action Recognition—0
Knowledge Prompting for Few-shot Action Recognition—0
Know your sensORs -- A Modality Study For Surgical Action Classification—0
Koala: Key frame-conditioned long video-LLM—0
KPCA Spatio-temporal trajectory point cloud classifier for recognizing human actions in a CBVR system—0
KShapeNet: Riemannian network on Kendall shape space for Skeleton based Action Recognition—0
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks—0
Label Efficient Learning of Transferable Representations across Domains and Tasks—0
PALM: Predicting Actions through Language Models—0
Language as the Medium: Multimodal Video Classification through text only—0
Language-based Action Concept Spaces Improve Video Self-Supervised Learning—0
LS-HAR: Language Supervised Human Action Recognition with Salient Fusion, Construction Sites as a Use-Case—0
Large Language Models for Video Surveillance Applications—0
Lattice Long Short-Term Memory for Human Action Recognition—0
LAVA: Language Audio Vision Alignment for Contrastive Video Pre-Training—0
LEAP: LLM-Generation of Egocentric Action Programs—0
Learn2Augment: Learning to Composite Videos for Data Augmentation in Action Recognition—0
Learnable Irrelevant Modality Dropout for Multimodal Action Recognition on Modality-Specific Annotated Videos—0
Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition—0
Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning—0
Learning Actionness via Long-range Temporal Order Verification—0
Learning Action Recognition Model From Depth and Skeleton Videos—0
Learning a Deep Model for Human Action Recognition from Novel Viewpoints—0
Learning a discriminative hidden part model for human action recognition—0
Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks—0
Learning and Refining of Privileged Information-based RNNs for Action Recognition from Depth Sequences—0
Learning and Using the Arrow of Time—0
Learning a Non-Linear Knowledge Transfer Model for Cross-View Action Recognition—0
Learning a Pose Lexicon for Semantic Action Recognition—0
Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition—0
Learning Class Regularized Features for Action Recognition—0
Learning clip representations for skeleton-based 3d action recognition—0
Show:102550
← PrevPage 52 of 56Next →
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