Action Spotting On Soccernet
Métriques
Average-mAP
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Average-mAP | Paper Title | Repository |
|---|---|---|---|
| Two-stream CNN + Dilated RNN (Mahaseni et al.) | 63.3 | Spotting Football Events Using Two-Stream Convolutional Neural Network and Dilated Recurrent Neural Network | - |
| CALF (Cioppa et al.) | 62.5 | A Context-Aware Loss Function for Action Spotting in Soccer Videos | |
| RMS-Net (Tomei et al.) | 75.1 | RMS-Net: Regression and Masking for Soccer Event Spotting | |
| 3D CNN (Rongved et al.) | 32.0 | Real-Time Detection of Events in Soccer Videosusing 3D Convolutional Neural Networks | - |
| AudioVid (Vanderplaetse et al.) | 56.0 | Improved Soccer Action Spotting using both Audio and Video Streams | - |
| NetVLAD (Giancola et al.) | 49.7 | SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos | |
| Multi-tower CNN (Vats et al.) | 60.1 | Event detection in coarsely annotated sports videos via parallel multi receptive field 1D convolutions | - |
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