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 926950 of 2759 papers

TitleStatusHype
Federated Action Recognition on Heterogeneous Embedded Devices0
Dynamic Matrix Decomposition for Action Recognition0
Dynamic Inference: A New Approach Toward Efficient Video Action Recognition0
Graph Convolutional Module for Temporal Action Localization in Videos0
Graph learning in robotics: a survey0
Chop & Learn: Recognizing and Generating Object-State Compositions0
Few-shot Action Recognition via Intra- and Inter-Video Information Maximization0
Bag of Visual Words and Fusion Methods for Action Recognition: Comprehensive Study and Good Practice0
Few-Shot Action Recognition with Compromised Metric via Optimal Transport0
Dynamic Hypergraph Convolutional Networks for Skeleton-Based Action Recognition0
Adversarial Domain Adaptation for Action Recognition Around the Clock0
Few Shot Activity Recognition Using Variational Inference0
Analysis of Real-Time Hostile Activitiy Detection from Spatiotemporal Features Using Time Distributed Deep CNNs, RNNs and Attention-Based Mechanisms0
Few-Shot Video Classification via Temporal Alignment0
Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition0
FILS: Self-Supervised Video Feature Prediction In Semantic Language Space0
fine-CLIP: Enhancing Zero-Shot Fine-Grained Surgical Action Recognition with Vision-Language Models0
Knowledge Distillation for Human Action Anticipation0
Gradient Weighted Superpixels for Interpretability in CNNs0
Fine-grained activity recognition for assembly videos0
Class-Incremental Learning for Action Recognition in Videos0
Fine-grained Multi-Modal Self-Supervised Learning0
Fine-Grained Side Information Guided Dual-Prompts for Zero-Shot Skeleton Action Recognition0
Fine-grained Video Categorization with Redundancy Reduction Attention0
Dynamic Appearance: A Video Representation for Action Recognition with Joint Training0
Show:102550
← PrevPage 38 of 111Next →

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