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 726750 of 2759 papers

TitleStatusHype
Multi-view knowledge distillation transformer for human action recognition0
3Mformer: Multi-order Multi-mode Transformer for Skeletal Action Recognition0
Towards Scalable Neural Representation for Diverse Videos0
A Large-scale Study of Spatiotemporal Representation Learning with a New Benchmark on Action RecognitionCode1
The effectiveness of MAE pre-pretraining for billion-scale pretrainingCode1
Self-distillation for surgical action recognitionCode1
Automatic evaluation of herding behavior in towed fishing gear using end-to-end training of CNN and attention-based networks0
ViC-MAE: Self-Supervised Representation Learning from Images and Video with Contrastive Masked AutoencodersCode0
Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition0
Augmenting and Aligning Snippets for Few-Shot Video Domain Adaptation0
Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline PerformancesCode1
Video Action Recognition with Attentive Semantic Units0
Dual-path Adaptation from Image to Video TransformersCode1
Action knowledge for video captioning with graph neural networksCode1
MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language KnowledgeCode1
EgoViT: Pyramid Video Transformer for Egocentric Action Recognition0
3DInAction: Understanding Human Actions in 3D Point CloudsCode1
HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action RepresentationsCode1
Challenges of the Creation of a Dataset for Vision Based Human Hand Action Recognition in Industrial Assembly0
Learning Discriminative Representations for Skeleton Based Action RecognitionCode1
Event Voxel Set Transformer for Spatiotemporal Representation Learning on Event StreamsCode0
CLIP-guided Prototype Modulating for Few-shot Action RecognitionCode1
Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition0
MITFAS: Mutual Information based Temporal Feature Alignment and Sampling for Aerial Video Action RecognitionCode0
AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning0
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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