HyperAI

Action Detection On Ucf101 24

Métriques

Video-mAP 0.2
Video-mAP 0.5

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
Video-mAP 0.2
Video-mAP 0.5
Paper TitleRepository
HISAN (ResNet-101 + FPN)82.3051.47Hierarchical Self-Attention Network for Action Localization in Videos-
MR-TS R-CNN--Multi-region two-stream R-CNN for action detection-
TS R-CNN--Multi-region two-stream R-CNN for action detection-
STEP76.6-STEP: Spatio-Temporal Progressive Learning for Video Action Detection
TACNet77.552.9TACNet: Transition-Aware Context Network for Spatio-Temporal Action Detection-
Faster-RCNN + two-stream I3D conv-59.9AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
HIT88.874.3Holistic Interaction Transformer Network for Action Detection
HISAN (VGG-16)80.4249.50Hierarchical Self-Attention Network for Action Localization in Videos-
YOWO75.848.8You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization-
MOC81.853.9Actions as Moving Points
T-CNN47.1-Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos
YOWO + LFB78.653.1You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization-
Two-in-one75.4848.31Dance with Flow: Two-in-One Stream Action Detection
Two-in-one Two Stream78.4850.30Dance with Flow: Two-in-One Stream Action Detection
DTS-54Finding Action Tubes with a Sparse-to-Dense Framework-
E2E-SSL (I3D)-72.1End-to-End Semi-Supervised Learning for Video Action Detection
Stable Mean Teacher (I3D)-76.3Stable Mean Teacher for Semi-supervised Video Action Detection-
STAR/L88.071.8End-to-End Spatio-Temporal Action Localisation with Video Transformers-
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