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 26512675 of 2759 papers

TitleStatusHype
Domain and View-point Agnostic Hand Action RecognitionCode0
DMCL: Distillation Multiple Choice Learning for Multimodal Action RecognitionCode0
Revisiting hand-crafted feature for action recognition: a set of improved dense trajectoriesCode0
2D/3D Pose Estimation and Action Recognition using Multitask Deep LearningCode0
Human Action Recognition by Representing 3D Skeletons as Points in a Lie GroupCode0
HPERL: 3D Human Pose Estimation from RGB and LiDARCode0
REPAIR: Removing Representation Bias by Dataset ResamplingCode0
Attention Bottlenecks for Multimodal FusionCode0
Synchronized and Fine-Grained Head for Skeleton-Based Ambiguous Action RecognitionCode0
Discriminating Spatial and Temporal Relevance in Deep Taylor Decompositions for Explainable Activity RecognitionCode0
RHM: Robot House Multi-view Human Activity Recognition DatasetCode0
Richly Activated Graph Convolutional Network for Action Recognition with Incomplete SkeletonsCode0
Richly Activated Graph Convolutional Network for Robust Skeleton-based Action RecognitionCode0
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural NetworksCode0
Attentional Pooling for Action RecognitionCode0
HopaDIFF: Holistic-Partial Aware Fourier Conditioned Diffusion for Referring Human Action Segmentation in Multi-Person ScenariosCode0
HomE: Homography-Equivariant Video Representation LearningCode0
H-MoRe: Learning Human-centric Motion Representation for Action AnalysisCode0
High-Performance Inference Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
Synthetic Humans for Action Recognition from Unseen ViewpointsCode0
DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action RecognitionCode0
Video Anomaly Detection by Estimating Likelihood of RepresentationsCode0
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity RecognitionCode0
TAda! Temporally-Adaptive Convolutions for Video UnderstandingCode0
Hierarchical growing grid networks for skeleton based action recognitionCode0
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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