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 1751–1800 of 2759 papers

TitleStatusHype
Action2Vec: A Crossmodal Embedding Approach to Action Learning—0
Action4D: Online Action Recognition in the Crowd and Clutter—0
Action4D: Real-time Action Recognition in the Crowd and Clutter—0
ActionAtlas: A VideoQA Benchmark for Domain-specialized Action Recognition—0
Action-Attending Graphic Neural Network—0
Action-Based ADHD Diagnosis in Video—0
Action Capsules: Human Skeleton Action Recognition—0
Action Classification via Concepts and Attributes—0
Action Class Relation Detection and Classification Across Multiple Video Datasets—0
Action Concept Grounding Network for Semantically-Consistent Video Generation—0
Modular Action Concept Grounding in Semantic Video Prediction—0
Action Detail Matters: Refining Video Recognition with Local Action Queries—0
Action Detection by Implicit Intentional Motion Clustering—0
ActionFlowNet: Learning Motion Representation for Action Recognition—0
Action Genome: Actions As Compositions of Spatio-Temporal Scene Graphs—0
ActionHub: A Large-scale Action Video Description Dataset for Zero-shot Action Recognition—0
Action in Mind: A Neural Network Approach to Action Recognition and Segmentation—0
Action Keypoint Network for Efficient Video Recognition—0
Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition—0
Action Localization through Continual Predictive Learning—0
Action Machine: Rethinking Action Recognition in Trimmed Videos—0
Actionness-Assisted Recognition of Actions—0
Actionness Estimation Using Hybrid Fully Convolutional Networks—0
Action Recognition and State Change Prediction in a Recipe Understanding Task Using a Lightweight Neural Network Model—0
Action Recognition based Industrial Safety Violation Detection—0
Action Recognition Based on Joint Trajectory Maps with Convolutional Neural Networks—0
Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks—0
Action Recognition based on Subdivision-Fusion Model—0
Action Recognition by Hierarchical Mid-level Action Elements—0
Action Recognition by Hierarchical Sequence Summarization—0
Action recognition by learning pose representations—0
Action Recognition for American Sign Language—0
Action recognition from depth maps using deep convolutional neural networks—0
Action Recognition: From Static Datasets to Moving Robots—0
Action recognition in real-world videos—0
Action recognition in still images by latent superpixel classification—0
Action Recognition in the Frequency Domain—0
Action Recognition in Untrimmed Videos with Composite Self-Attention Two-Stream Framework—0
Action Recognition in Video Recordings from Gynecologic Laparoscopy—0
Action Recognition in Video Using Sparse Coding and Relative Features—0
Action Recognition Using Supervised Spiking Neural Networks—0
Action Recognition using Transfer Learning and Majority Voting for CSGO—0
Action Recognition Utilizing YGAR Dataset—0
Action Recognition via Pose-Based Graph Convolutional Networks with Intermediate Dense Supervision—0
Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion—0
Action Recognition with Deep Multiple Aggregation Networks—0
Action Recognition with Domain Invariant Features of Skeleton Image—0
Action Recognition with Image Based CNN Features—0
Action Recognition with Joint Attention on Multi-Level Deep Features—0
Action Recognition with Kernel-based Graph Convolutional Networks—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