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

Showing 501550 of 2759 papers

TitleStatusHype
Unifying Nonlocal Blocks for Neural NetworksCode1
UNIK: A Unified Framework for Real-world Skeleton-based Action RecognitionCode1
Disentangled Non-Local Neural NetworksCode1
Unsupervised Visual Representation Learning by Tracking Patches in VideoCode1
USDRL: Unified Skeleton-Based Dense Representation Learning with Multi-Grained Feature DecorrelationCode1
Diverse Temporal Aggregation and Depthwise Spatiotemporal Factorization for Efficient Video ClassificationCode1
ViA: View-invariant Skeleton Action Representation Learning via Motion RetargetingCode1
Complex Sequential Understanding through the Awareness of Spatial and Temporal ConceptsCode1
Can An Image Classifier Suffice For Action Recognition?Code1
Busy-Quiet Video Disentangling for Video ClassificationCode1
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video RecognitionCode1
Animal Kingdom: A Large and Diverse Dataset for Animal Behavior UnderstandingCode1
DEVIAS: Learning Disentangled Video Representations of Action and SceneCode1
ConvNet Architecture Search for Spatiotemporal Feature LearningCode1
An Image is Worth 16x16 Words, What is a Video Worth?Code1
Compressing Recurrent Neural Networks with Tensor Ring for Action RecognitionCode1
Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video DatasetCode1
Disentangled Pre-training for Human-Object Interaction DetectionCode1
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical AggregationCode1
Domain Knowledge-Informed Self-Supervised Representations for Workout Form AssessmentCode1
DSANet: Dynamic Segment Aggregation Network for Video-Level Representation LearningCode1
DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology ModelingCode1
DirecFormer: A Directed Attention in Transformer Approach to Robust Action RecognitionCode1
Conquering the cnn over-parameterization dilemma: A volterra filtering approach for action recognitionCode1
Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action RecognitionCode1
Constructing Stronger and Faster Baselines for Skeleton-based Action RecognitionCode1
Do Language Models Understand Time?Code1
EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal TokensCode1
EAN: Event Adaptive Network for Enhanced Action RecognitionCode1
Anonymization for Skeleton Action RecognitionCode1
Exploring Few-Shot Adaptation for Activity Recognition on Diverse DomainsCode1
EgoSurgery-Phase: A Dataset of Surgical Phase Recognition from Egocentric Open Surgery VideosCode1
EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the BackboneCode1
Elaborative Rehearsal for Zero-shot Action RecognitionCode1
Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the MotionCode1
Enlarging Instance-specific and Class-specific Information for Open-set Action RecognitionCode1
EPFL-Smart-Kitchen-30: Densely annotated cooking dataset with 3D kinematics to challenge video and language modelsCode1
Epic-Sounds: A Large-scale Dataset of Actions That SoundCode1
Eventful Transformers: Leveraging Temporal Redundancy in Vision TransformersCode1
EventRPG: Event Data Augmentation with Relevance Propagation GuidanceCode1
Hierarchical Contrast for Unsupervised Skeleton-based Action Representation LearningCode1
ExACT: Language-guided Conceptual Reasoning and Uncertainty Estimation for Event-based Action Recognition and MoreCode1
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action RecognitionCode1
Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action RecognitionCode1
Counterfactual Debiasing Inference for Compositional Action RecognitionCode1
Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal DetectionCode1
Few-shot Action Recognition with Prototype-centered Attentive LearningCode1
Multi-Granularity Hand Action DetectionCode1
FLAVR: Flow-Agnostic Video Representations for Fast Frame InterpolationCode1
Learning Self-Similarity in Space and Time as Generalized Motion for Video Action RecognitionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MViTv2-B (IN-21K + Kinetics400 pretrain)Top-5 Accuracy93.4Unverified
2RSANet-R50 (8+16 frames, ImageNet pretrained, 2 clips)Top-5 Accuracy91.1Unverified
3MVD (Kinetics400 pretrain, ViT-H, 16 frame)Top-1 Accuracy77.3Unverified
4InternVideoTop-1 Accuracy77.2Unverified
5DejaVidTop-1 Accuracy77.2Unverified
6InternVideo2-1BTop-1 Accuracy77.1Unverified
7VideoMAE V2-gTop-1 Accuracy77Unverified
8MVD (Kinetics400 pretrain, ViT-L, 16 frame)Top-1 Accuracy76.7Unverified
9Hiera-L (no extra data)Top-1 Accuracy76.5Unverified
10TubeViT-LTop-1 Accuracy76.1Unverified
#ModelMetricClaimedVerifiedStatus
1FTP-UniFormerV2-L/143-fold Accuracy99.7Unverified
2OmniVec23-fold Accuracy99.6Unverified
3OmniVec3-fold Accuracy99.6Unverified
4VideoMAE V2-g3-fold Accuracy99.6Unverified
5BIKE3-fold Accuracy98.8Unverified
6SMART3-fold Accuracy98.64Unverified
7ZeroI2V ViT-L/143-fold Accuracy98.6Unverified
8OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6Unverified
9PERF-Net (multi-distilled S3D)3-fold Accuracy98.6Unverified
10Text4Vis3-fold Accuracy98.2Unverified