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 14011425 of 2759 papers

TitleStatusHype
Self-supervised Video Representation Learning by Context and Motion DecouplingCode0
UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial VehiclesCode1
Beyond Short Clips: End-to-End Video-Level Learning with Collaborative Memories0
Self-supervised Motion Learning from Static Images0
Selective Feature Compression for Efficient Activity Recognition Inference0
Composable Augmentation Encoding for Video Representation Learning0
Motion Guided Attention Fusion to Recognize Interactions from Videos0
Weakly Supervised Temporal Action Localization Through Learning Explicit Subspaces for Action and Context0
Recognizing Actions in Videos from Unseen Viewpoints0
Learning Representational Invariances for Data-Efficient Action RecognitionCode1
Robust Audio-Visual Instance Discrimination0
Busy-Quiet Video Disentangling for Video ClassificationCode1
ViViT: A Video Vision TransformerCode1
No frame left behind: Full Video Action RecognitionCode1
GPRAR: Graph Convolutional Network based Pose Reconstruction and Action Recognition for Human Trajectory PredictionCode0
An Image is Worth 16x16 Words, What is a Video Worth?Code1
Learning Comprehensive Motion Representation for Action Recognition0
AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition0
MoViNets: Mobile Video Networks for Efficient Video RecognitionCode1
Efficient Spatialtemporal Context Modeling for Action Recognition0
Deep Learning for Vision-Based Fall Detection System: Enhanced Optical Dynamic Flow0
CLTA: Contents and Length-based Temporal Attention for Few-shot Action Recognition0
NAS-TC: Neural Architecture Search on Temporal Convolutions for Complex Action Recognition0
ACTION-Net: Multipath Excitation for Action RecognitionCode1
VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial ExamplesCode1
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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