Zero-Shot Grounding of Objects from Natural Language Queries
Table 2: Comparison of our model with other state of the art methods. We denote those networks which use classification weights from ImageNet [41] using “cls” and those networks which use detection weights from Pascal VOC [12] using “det”. The reported numbers are all Accuracy@IoU=0.5 or equivalently Recall@1. Models marked with “*” fine-tune their detection network on the entities in the Flickr30k.
['Method', 'Net', 'Flickr30k', 'ReferIt']
Table 2 compares ZSGNet with prior works on Flickr30k Entities  and ReferIt . We use "det" and "cls" to denote models using Pascal VOC  detection weights and ImageNet [10, 41] classification weights. Networks marked [CONTINUE] with "*" fine-tune their object detector pretrained on PascalVOC  on the fixed entities of Flickr30k . [CONTINUE] However, such information is not available in ReferIt dataset which explains ∼ 9% increase in performance of ZSGNet over other methods.