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
MILES: Visual BERT Pre-training with Injected Language Semantics for Video-text RetrievalCode1
Animal Kingdom: A Large and Diverse Dataset for Animal Behavior UnderstandingCode1
Mitigating Representation Bias in Action Recognition: Algorithms and BenchmarksCode1
MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language KnowledgeCode1
Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation LearningCode1
Masked Motion Predictors are Strong 3D Action Representation LearnersCode1
MAU: A Motion-Aware Unit for Video Prediction and BeyondCode1
Complex Sequential Understanding through the Awareness of Spatial and Temporal ConceptsCode1
Can An Image Classifier Suffice For Action Recognition?Code1
Memory-augmented Dense Predictive Coding for Video Representation LearningCode1
ConvNet Architecture Search for Spatiotemporal Feature LearningCode1
An Image is Worth 16x16 Words, What is a Video Worth?Code1
CZU-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and 10 wearable inertial sensorsCode1
MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless SensingCode1
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical AggregationCode1
Compressing Recurrent Neural Networks with Tensor Ring for Action RecognitionCode1
Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video DatasetCode1
Counterfactual Debiasing Inference for Compositional Action RecognitionCode1
Concatenated Masked Autoencoders as Spatial-Temporal LearnerCode1
Cross-Architecture Self-supervised Video Representation LearningCode1
Motion Representation Using Residual Frames with 3D CNNCode1
MotionSqueeze: Neural Motion Feature Learning for Video UnderstandingCode1
Volterra Neural Networks (VNNs)Code1
Conquering the cnn over-parameterization dilemma: A volterra filtering approach for action recognitionCode1
CT-Net: Channel Tensorization Network for Video ClassificationCode1
Constructing Stronger and Faster Baselines for Skeleton-based Action RecognitionCode1
Multi-dataset Training of Transformers for Robust Action RecognitionCode1
DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action RecognitionCode1
Context-Aware RCNN: A Baseline for Action Detection in VideosCode1
Anonymization for Skeleton Action RecognitionCode1
Deep Analysis of CNN-based Spatio-temporal Representations for Action RecognitionCode1
Multimodal Distillation for Egocentric Action RecognitionCode1
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action RecognitionCode1
Decoupling GCN with DropGraph Module for Skeleton-Based Action RecognitionCode1
DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 PandemicCode1
Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action RecognitionCode1
Deep Multimodal Feature Encoding for Video OrderingCode1
Multi-Semantic Fusion Model for Generalized Zero-Shot Skeleton-Based Action RecognitionCode1
Mutual Modality Learning for Video Action ClassificationCode1
No frame left behind: Full Video Action RecognitionCode1
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video RecognitionCode1
MVFNet: Multi-View Fusion Network for Efficient Video RecognitionCode1
PAN: Towards Fast Action Recognition via Learning Persistence of AppearanceCode1
Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action RecognitionCode1
DEVIAS: Learning Disentangled Video Representations of Action and SceneCode1
DirecFormer: A Directed Attention in Transformer Approach to Robust Action RecognitionCode1
Disentangled Non-Local Neural NetworksCode1
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action RecognitionCode1
Disentangled Pre-training for Human-Object Interaction DetectionCode1
Rescaling Egocentric VisionCode1
Show:102550
← PrevPage 11 of 56Next →

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