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 251275 of 2759 papers

TitleStatusHype
Logsig-RNN: a novel network for robust and efficient skeleton-based action recognitionCode1
Look More but Care Less in Video RecognitionCode1
EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal TokensCode1
Can An Image Classifier Suffice For Action Recognition?Code1
Exploring Few-Shot Adaptation for Activity Recognition on Diverse DomainsCode1
Actions as Moving PointsCode1
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual ActionsCode1
Evidential Deep Learning for Open Set Action RecognitionCode1
EventRPG: Event Data Augmentation with Relevance Propagation GuidanceCode1
AutoVideo: An Automated Video Action Recognition SystemCode1
Event Stream based Human Action Recognition: A High-Definition Benchmark Dataset and AlgorithmsCode1
ExACT: Language-guided Conceptual Reasoning and Uncertainty Estimation for Event-based Action Recognition and MoreCode1
Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal DetectionCode1
Audio-Visual Instance Discrimination with Cross-Modal AgreementCode1
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
Attention Prompt Tuning: Parameter-efficient Adaptation of Pre-trained Models for Spatiotemporal ModelingCode1
Enlarging Instance-specific and Class-specific Information for Open-set Action RecognitionCode1
EPAM-Net: An Efficient Pose-driven Attention-guided Multimodal Network for Video Action RecognitionCode1
Mitigating and Evaluating Static Bias of Action Representations in the Background and the ForegroundCode1
3DInAction: Understanding Human Actions in 3D Point CloudsCode1
A Unified Multimodal De- and Re-coupling Framework for RGB-D Motion RecognitionCode1
AutoLabel: CLIP-based framework for Open-set Video Domain AdaptationCode1
ViNet: Pushing the limits of Visual Modality for Audio-Visual Saliency PredictionCode1
End-to-End Learning of Visual Representations from Uncurated Instructional VideosCode1
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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