Using a visual model with strong generalization capability can improve event-recognition accuracy by 5%.

High-capacity vision models trained on large datasets generalize better, reducing overfitting and improving accuracy across event-detection tasks. Empirical results (e.g., ImageNet pretrained models) support measurable accuracy gains of around 5% in multi-scene recognition.