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 25262550 of 2759 papers

TitleStatusHype
Balanced Representation Learning for Long-tailed Skeleton-based Action RecognitionCode0
Action Recognition Using Volumetric Motion RepresentationsCode0
Towards Improving Spatiotemporal Action Recognition in VideosCode0
Back to the Future: Cycle Encoding Prediction for Self-supervised Contrastive Video Representation LearningCode0
Enhancing Split Computing and Early Exit Applications through Predefined SparsityCode0
Learning Gating ConvNet for Two-Stream based Methods in Action RecognitionCode0
Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action RecognitionCode0
Action Recognition using Visual AttentionCode0
Analysis and Extensions of Adversarial Training for Video ClassificationCode0
A Video-based End-to-end Pipeline for Non-nutritive Sucking Action Recognition and Segmentation in Young InfantsCode0
Unsupervised Video Domain Adaptation with Masked Pre-Training and Collaborative Self-TrainingCode0
Learning from Video and Text via Large-Scale Discriminative ClusteringCode0
Enhancing human action recognition with GAN-based data augmentationCode0
Spatio-Temporal Naive-Bayes Nearest-Neighbor (ST-NBNN) for Skeleton-Based Action RecognitionCode0
ProMQA: Question Answering Dataset for Multimodal Procedural Activity UnderstandingCode0
Skeleton-OOD: An End-to-End Skeleton-Based Model for Robust Out-of-Distribution Human Action DetectionCode0
Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action RecognitionCode0
Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action RecognitionCode0
Video Transformer NetworkCode0
Enhancing Action Recognition by Leveraging the Hierarchical Structure of Actions and Textual ContextCode0
PSO-Convolutional Neural Networks with Heterogeneous Learning RateCode0
Learning deep representations for video-based intake gesture detectionCode0
Learning Actor Relation Graphs for Group Activity RecognitionCode0
End-to-end Video-level Representation Learning for Action RecognitionCode0
Large-scale weakly-supervised pre-training for video action recognitionCode0
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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
4DejaVidTop-1 Accuracy77.2Unverified
5InternVideoTop-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
3VideoMAE V2-g3-fold Accuracy99.6Unverified
4OmniVec3-fold Accuracy99.6Unverified
5BIKE3-fold Accuracy98.8Unverified
6SMART3-fold Accuracy98.64Unverified
7OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6Unverified
8PERF-Net (multi-distilled S3D)3-fold Accuracy98.6Unverified
9ZeroI2V ViT-L/143-fold Accuracy98.6Unverified
10LGD-3D Two-stream3-fold Accuracy98.2Unverified