Temporal Localization of Moments in Video Collections with Natural Language
Table 5: Video (top) and language (bottom) feature ablation. DiDeMo (val) results on our CAL model. See text for details.
['Modality', 'Feature', 'IoU=0.5 K=1', 'IoU=0.5 K=10', 'IoU=0.5 K=100', 'IoU=0.5 MR ↓', 'IoU=0.7 K=1', 'IoU=0.7 K=10', 'IoU=0.7 K=100', 'IoU=0.7 MR ↓']
Which video features perform best? We use our CAL model as the base model with Glove language features; we do not use temporal endpoint features. We use the same process outlined in Section 4.1 to compute the ResNet and VGG features (c.f. paragraph implementation details in main submission).