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 351400 of 2759 papers

TitleStatusHype
Action knowledge for video captioning with graph neural networksCode1
AutoLabel: CLIP-based framework for Open-set Video Domain AdaptationCode1
Just Add π! Pose Induced Video Transformers for Understanding Activities of Daily LivingCode1
Keeping Your Eye on the Ball: Trajectory Attention in Video TransformersCode1
C2C: Component-to-Composition Learning for Zero-Shot Compositional Action RecognitionCode1
AutoVideo: An Automated Video Action Recognition SystemCode1
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual ActionsCode1
Cross-Architecture Self-supervised Video Representation LearningCode1
DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action RecognitionCode1
Learning Discriminative Representations for Skeleton Based Action RecognitionCode1
AViD Dataset: Anonymized Videos from Diverse CountriesCode1
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D NetworksCode1
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action RecognitionCode1
Learning Representational Invariances for Data-Efficient Action RecognitionCode1
Context-Aware RCNN: A Baseline for Action Detection in VideosCode1
Learning Self-Similarity in Space and Time as Generalized Motion for Video Action RecognitionCode1
Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action RecognitionCode1
Anonymization for Skeleton Action RecognitionCode1
Conquering the cnn over-parameterization dilemma: A volterra filtering approach for action recognitionCode1
Learning Temporally Invariant and Localizable Features via Data Augmentation for Video RecognitionCode1
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense EvaluationCode1
Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D SpaceCode1
Leveraging Spatio-Temporal Dependency for Skeleton-Based Action RecognitionCode1
Constructing Stronger and Faster Baselines for Skeleton-based Action RecognitionCode1
ConvNet Architecture Search for Spatiotemporal Feature LearningCode1
Listen to Look: Action Recognition by Previewing AudioCode1
Location-aware Graph Convolutional Networks for Video Question AnsweringCode1
BASAR:Black-box Attack on Skeletal Action RecognitionCode1
LSTC: Boosting Atomic Action Detection with Long-Short-Term ContextCode1
M2A: Motion Aware Attention for Accurate Video Action RecognitionCode1
Data Efficient Video Transformer for Violence DetectionCode1
Mamba4D: Efficient 4D Point Cloud Video Understanding with Disentangled Spatial-Temporal State Space ModelsCode1
3D CNNs with Adaptive Temporal Feature ResolutionsCode1
Benchmarking Micro-action Recognition: Dataset, Methods, and ApplicationsCode1
Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the ArtCode1
Compressing Recurrent Neural Networks with Tensor Ring for Action RecognitionCode1
MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video RecognitionCode1
BEVT: BERT Pretraining of Video TransformersCode1
MGSampler: An Explainable Sampling Strategy for Video Action RecognitionCode1
MILES: Visual BERT Pre-training with Injected Language Semantics for Video-text RetrievalCode1
Complex Sequential Understanding through the Awareness of Spatial and Temporal ConceptsCode1
Motion-aware Contrastive Video Representation Learning via Foreground-background MergingCode1
Motion meets Attention: Video Motion PromptsCode1
Motion Representation Using Residual Frames with 3D CNNCode1
CDFSL-V: Cross-Domain Few-Shot Learning for VideosCode1
MoViNets: Mobile Video Networks for Efficient Video RecognitionCode1
MS^2L: Multi-Task Self-Supervised Learning for Skeleton Based Action RecognitionCode1
MSAF: Multimodal Split Attention FusionCode1
Multimodal Fusion via Teacher-Student Network for Indoor Action RecognitionCode1
Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video DatasetCode1
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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
2OmniVec3-fold Accuracy99.6Unverified
3VideoMAE V2-g3-fold Accuracy99.6Unverified
4OmniVec23-fold Accuracy99.6Unverified
5BIKE3-fold Accuracy98.8Unverified
6SMART3-fold Accuracy98.64Unverified
7ZeroI2V ViT-L/143-fold Accuracy98.6Unverified
8PERF-Net (multi-distilled S3D)3-fold Accuracy98.6Unverified
9OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6Unverified
10Text4Vis3-fold Accuracy98.2Unverified