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 15261550 of 2759 papers

TitleStatusHype
Conditional Extreme Value Theory for Open Set Video Domain AdaptationCode0
LIGAR: Lightweight General-purpose Action Recognition0
Shifted Chunk Transformer for Spatio-Temporal Representational Learning0
Learning Cross-modal Contrastive Features for Video Domain Adaptation0
BiaSwap: Removing dataset bias with bias-tailored swapping augmentation0
MM-ViT: Multi-Modal Video Transformer for Compressed Video Action Recognition0
Few Shot Activity Recognition Using Variational Inference0
Self-Supervised Video Representation Learning with Meta-Contrastive Network0
The Multi-Modal Video Reasoning and Analyzing Competition0
Channel-Temporal Attention for First-Person Video Domain Adaptation0
Learning Skeletal Graph Neural Networks for Hard 3D Pose EstimationCode0
Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-LearningCode0
Temporal Action Segmentation with High-level Complex Activity Labels0
Learning Visual Affordance Grounding from Demonstration Videos0
Spatio-Temporal Human Action Recognition Modelwith Flexible-interval Sampling and Normalization0
Temporal Action Localization Using Gated Recurrent UnitsCode0
Feature-Supervised Action Modality Transfer0
Skeleton Cloud Colorization for Unsupervised 3D Action Representation Learning0
Classifying action correctness in physical rehabilitation exercises0
Lighter Stacked Hourglass Human Pose Estimation0
A New Split for Evaluating True Zero-Shot Action RecognitionCode0
Adaptive Recursive Circle Framework for Fine-grained Action Recognition0
Federated Action Recognition on Heterogeneous Embedded Devices0
LSFB-CONT and LSFB-ISOL: Two New Datasets for Vision-Based Sign Language RecognitionCode0
Group Activity Recognition Using Joint Learning of Individual Action Recognition and People GroupingCode0
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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