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 1651–1700 of 2759 papers

TitleStatusHype
Texture-Based Input Feature Selection for Action Recognition—0
Videoprompter: an ensemble of foundational models for zero-shot video understanding—0
Video Representation Learning by Recognizing Temporal Transformations—0
Video Representation Learning Using Discriminative Pooling—0
Video-ReTime: Learning Temporally Varying Speediness for Time Remapping—0
Video RWKV:Video Action Recognition Based RWKV—0
Videos as Space-Time Region Graphs—0
Video Segmentation Learning Using Cascade Residual Convolutional Neural Network—0
Video Summarisation with Incident and Context Information using Generative AI—0
A Mobile Robot Generating Video Summaries of Seniors' Indoor Activities—0
Video-to-Task Learning via Motion-Guided Attention for Few-Shot Action Recognition—0
VidTr: Video Transformer Without Convolutions—0
View-invariant action recognition—0
View-invariant Deep Architecture for Human Action Recognition using late fusion—0
View invariant human action recognition using histograms of 3D joints—0
View-Invariant Skeleton-based Action Recognition via Global-Local Contrastive Learning—0
View-LSTM: Novel-View Video Synthesis Through View Decomposition—0
Viewpoint Invariant Action Recognition using RGB-D Videos—0
Viewpoint Rosetta Stone: Unlocking Unpaired Ego-Exo Videos for View-invariant Representation Learning—0
Vi-MIX FOR SELF-SUPERVISED VIDEO REPRESENTATION—0
Violence Recognition from Videos using Deep Learning Techniques—0
Vision and Inertial Sensing Fusion for Human Action Recognition : A Review—0
Construction Site Safety Monitoring and Excavator Activity Analysis System—0
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision—0
Vision Transformers for Action Recognition: A Survey—0
Vision Transformer with Cross-attention by Temporal Shift for Efficient Action Recognition—0
Visual Attribute-augmented Three-dimensional Convolutional Neural Network for Enhanced Human Action Recognition—0
Visual Concept Reasoning Networks—0
Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos—0
VLAD3: Encoding Dynamics of Deep Features for Action Recognition—0
VLM-Eval: A General Evaluation on Video Large Language Models—0
Wavelet-Decoupling Contrastive Enhancement Network for Fine-Grained Skeleton-Based Action Recognition—0
Weakly-Supervised Action Localization and Action Recognition using Global-Local Attention of 3D CNN—0
Weakly-supervised Compositional FeatureAggregation for Few-shot Recognition—0
Weakly-Supervised Multi-Person Action Recognition in 360^ Videos—0
Weakly Supervised Recognition of Surgical Gestures—0
Weakly Supervised Temporal Action Localization Through Learning Explicit Subspaces for Action and Context—0
We Can "See" You via Wi-Fi - WiFi Action Recognition via Vision-based Methods—0
What can a cook in Italy teach a mechanic in India? Action Recognition Generalisation Over Scenarios and Locations—0
What do 15,000 Object Categories Tell Us About Classifying and Localizing Actions?—0
What have we learned from deep representations for action recognition?—0
When Kernel Methods meet Feature Learning: Log-Covariance Network for Action Recognition from Skeletal Data—0
When Spatial meets Temporal in Action Recognition—0
Where and when to look? Spatial-temporal attention for action recognition in videos—0
WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos—0
Word-level Sign Language Recognition with Multi-stream Neural Networks Focusing on Local Regions and Skeletal Information—0
X-ToM: Explaining with Theory-of-Mind for Gaining Justified Human Trust—0
X-VARS: Introducing Explainability in Football Refereeing with Multi-Modal Large Language Model—0
YH Technologies at ActivityNet Challenge 2018—0
You Lead, We Exceed: Labor-Free Video Concept Learning by Jointly Exploiting Web Videos and Images—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