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 27012725 of 2759 papers

TitleStatusHype
Manta: Enhancing Mamba for Few-Shot Action Recognition of Long Sub-Sequence0
MAPLE: Masked Pseudo-Labeling autoEncoder for Semi-supervised Point Cloud Action Recognition0
MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning0
Activity Recognition on Avatar-Anonymized Datasets with Masked Differential Privacy0
Masked Video and Body-worn IMU Autoencoder for Egocentric Action Recognition0
Massively Parallel Video Networks0
Matrix Manifold Neural Networks++0
Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition0
Measuring Student Behavioral Engagement using Histogram of Actions0
MECCANO: A Multimodal Egocentric Dataset for Humans Behavior Understanding in the Industrial-like Domain0
Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment0
Memory-Augmented Temporal Dynamic Learning for Action Recognition0
Memory Group Sampling Based Online Action Recognition Using Kinetic Skeleton Features0
Metric-Based Few-Shot Learning for Video Action Recognition0
MFAS: Multimodal Fusion Architecture Search0
MiCT: Mixed 3D/2D Convolutional Tube for Human Action Recognition0
Mimetics: Towards Understanding Human Actions Out of Context0
Mimic The Raw Domain: Accelerating Action Recognition in the Compressed Domain0
Miniaturized Graph Convolutional Networks with Topologically Consistent Pruning0
Mining 3D Key-Pose-Motifs for Action Recognition0
Mining Mid-level Features for Action Recognition Based on Effective Skeleton Representation0
Mix Dimension in Poincaré Geometry for 3D Skeleton-based Action Recognition0
MixTConv: Mixed Temporal Convolutional Kernels for Efficient Action Recogntion0
MK-SGN: A Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation for Skeleton-based Action Recognition0
MLGCN: Multi-Laplacian Graph Convolutional Networks for Human Action Recognition0
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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