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 11511175 of 2759 papers

TitleStatusHype
Tell me what you see: A zero-shot action recognition method based on natural language descriptionsCode1
Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition0
Self-attention based anchor proposal for skeleton-based action recognition0
Distillation of Human-Object Interaction Contexts for Action Recognition0
Analysis and Evaluation of Kinect-based Action Recognition AlgorithmsCode0
Masked Feature Prediction for Self-Supervised Visual Pre-TrainingCode1
Detecting Object States vs Detecting Objects: A New Dataset and a Quantitative Experimental StudyCode0
Temporal Shuffling for Defending Deep Action Recognition Models against Adversarial AttacksCode0
Temporal Transformer Networks with Self-Supervision for Action Recognition0
Co-training Transformer with Videos and Images Improves Action Recognition0
Multi-Expert Human Action Recognition with Hierarchical Super-Class Learning0
Real Time Action Recognition from Video Footage0
SVIP: Sequence VerIfication for Procedures in VideosCode1
Self-supervised Spatiotemporal Representation Learning by Exploiting Video Continuity0
Spatio-temporal Relation Modeling for Few-shot Action RecognitionCode1
Contextualized Spatio-Temporal Contrastive Learning with Self-SupervisionCode0
Topology-aware Convolutional Neural Network for Efficient Skeleton-based Action RecognitionCode1
Prompting Visual-Language Models for Efficient Video UnderstandingCode1
Cross-modal Manifold Cutmix for Self-supervised Video Representation Learning0
TCGL: Temporal Contrastive Graph for Self-supervised Video Representation LearningCode1
E^2(GO)MOTION: Motion Augmented Event Stream for Egocentric Action RecognitionCode1
Time-Equivariant Contrastive Video Representation Learning0
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action RecognitionCode1
Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition0
STSM: Spatio-Temporal Shift Module for Efficient Action Recognition0
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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