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
Human activity recognition from skeleton posesCode0
I3D-LSTM: A New Model for Human Action RecognitionCode0
Improving Skeleton-based Action Recognition with Interactive Object InformationCode0
FPGA-QHAR: Throughput-Optimized for Quantized Human Action Recognition on The EdgeCode0
Two-person Graph Convolutional Network for Skeleton-based Human Interaction RecognitionCode0
Two-stream Flow-guided Convolutional Attention Networks for Action RecognitionCode0
HomE: Homography-Equivariant Video Representation LearningCode0
HopaDIFF: Holistic-Partial Aware Fourier Conditioned Diffusion for Referring Human Action Segmentation in Multi-Person ScenariosCode0
HPERL: 3D Human Pose Estimation from RGB and LiDARCode0
Actional-Structural Graph Convolutional Networks for Skeleton-based Action RecognitionCode0
Uncertainty-DTW for Time Series and SequencesCode0
Convolutional Two-Stream Network Fusion for Video Action RecognitionCode0
Appearance-and-Relation Networks for Video ClassificationCode0
H-MoRe: Learning Human-centric Motion Representation for Action AnalysisCode0
ConvGRU in Fine-grained Pitching Action Recognition for Action Outcome PredictionCode0
A Perceptual Prediction Framework for Self Supervised Event SegmentationCode0
Action Recognition with Trajectory-Pooled Deep-Convolutional DescriptorsCode0
Hierarchical growing grid networks for skeleton based action recognitionCode0
Hierarchical Explanations for Video Action RecognitionCode0
High-Performance Inference Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
Human Action Recognition by Representing 3D Skeletons as Points in a Lie GroupCode0
Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition with Multimodal TrainingCode0
FT-HID: A Large Scale RGB-D Dataset for First and Third Person Human Interaction AnalysisCode0
HA-ViD: A Human Assembly Video Dataset for Comprehensive Assembly Knowledge UnderstandingCode0
HARFLOW3D: A Latency-Oriented 3D-CNN Accelerator Toolflow for HAR on FPGA DevicesCode0
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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