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 9761000 of 2759 papers

TitleStatusHype
Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action RecognitionCode0
LAVA: Language Audio Vision Alignment for Contrastive Video Pre-Training0
Action Recognition With Motion Diversification and Dynamic Selection0
BQN: Busy-Quiet Net Enabled by Motion Band-Pass Module for Action Recognition0
Is Appearance Free Action Recognition Possible?Code1
Skeletal Human Action Recognition using Hybrid Attention based Graph Convolutional NetworkCode0
Efficient Human Vision Inspired Action Recognition using Adaptive Spatiotemporal SamplingCode0
Compound Prototype Matching for Few-shot Action Recognition0
VidConv: A modernized 2D ConvNet for Efficient Video RecognitionCode0
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2022: Team HNU-FPV Technical Report0
Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action RecognitionCode1
Unsupervised Learning for Human Sensing Using Radio Signals0
Federated Self-supervised Learning for Video UnderstandingCode1
Large-scale Robustness Analysis of Video Action Recognition ModelsCode0
Revisiting Classifier: Transferring Vision-Language Models for Video RecognitionCode2
Disentangled Action Recognition with Knowledge Bases0
Skeleton-based Action Recognition via Adaptive Cross-Form LearningCode0
Spatial Transformer Network with Transfer Learning for Small-scale Fine-grained Skeleton-based Tai Chi Action Recognition0
A New Adjacency Matrix Configuration in GCN-based Models for Skeleton-based Action Recognition0
ST-Adapter: Parameter-Efficient Image-to-Video Transfer LearningCode1
Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action RecognitionCode1
Defending Multimodal Fusion Models against Single-Source Adversaries0
Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D SpaceCode1
M&M Mix: A Multimodal Multiview Transformer Ensemble0
0/1 Deep Neural Networks via Block Coordinate Descent0
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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