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 351375 of 2759 papers

TitleStatusHype
Fine-Grained Side Information Guided Dual-Prompts for Zero-Shot Skeleton Action Recognition0
An Animation-based Augmentation Approach for Action Recognition from Discontinuous VideoCode0
O-TALC: Steps Towards Combating Oversegmentation within Online Action Segmentation0
ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised Action Recognition in VideosCode0
TIM: A Time Interval Machine for Audio-Visual Action RecognitionCode2
X-VARS: Introducing Explainability in Football Refereeing with Multi-Modal Large Language Model0
Koala: Key frame-conditioned long video-LLM0
PhysPT: Physics-aware Pretrained Transformer for Estimating Human Dynamics from Monocular Videos0
Learning Correlation Structures for Vision Transformers0
Multi-Scale Spatial-Temporal Self-Attention Graph Convolutional Networks for Skeleton-based Action Recognition0
Disentangled Pre-training for Human-Object Interaction DetectionCode1
Language Model Guided Interpretable Video Action ReasoningCode0
Leveraging YOLO-World and GPT-4V LMMs for Zero-Shot Person Detection and Action Recognition in Drone Imagery0
PREGO: online mistake detection in PRocedural EGOcentric videosCode1
LLMs are Good Action Recognizers0
A Unified Framework for Human-centric Point Cloud Video Understanding0
Hypergraph-based Multi-View Action Recognition using Event Cameras0
OmniVid: A Generative Framework for Universal Video UnderstandingCode2
DeGCN: Deformable Graph Convolutional Networks for Skeleton-Based Action RecognitionCode2
Understanding Long Videos with Multimodal Language ModelsCode2
Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with ObjectsCode3
Enhancing Video Transformers for Action Understanding with VLM-aided Training0
Emotion Recognition from the perspective of Activity Recognition0
GCN-DevLSTM: Path Development for Skeleton-Based Action RecognitionCode1
InternVideo2: Scaling Foundation Models for Multimodal Video UnderstandingCode7
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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